diff --git a/.gitignore b/.gitignore
index dc49827b..ef80c474 100644
--- a/.gitignore
+++ b/.gitignore
@@ -248,6 +248,8 @@ instance/
 # Sphinx documentation
 docs/_build/
 doc/_build/
+docs/examples/trained_model.sav
+docs/source/auto_examples/
 
 # PyBuilder
 target/
diff --git a/Pipfile b/Pipfile
index 4a08c49b..70f6ab34 100644
--- a/Pipfile
+++ b/Pipfile
@@ -6,16 +6,30 @@ name = "pypi"
 [packages]
 networkx = "*"
 numpy = "*"
-scikit-learn = "*"
+scikit-learn = "1.4.2"
+scipy = "1.11.4"
 
 [dev-packages]
-pytest = "*"
-pytest-flake8 = "*"
-pytest-pydocstyle = "*"
-pytest-cov = "*"
+pytest = "7.1.2"
+flake8 = "4.0.1"
+pytest-flake8 = "1.1.1"
+pydocstyle = "6.1.1"
+pytest-pydocstyle = "2.3.0"
+pytest-cov = "3.0.0"
 twine = "*"
-sphinx = "4.1.1"
-sphinx-rtd-theme = "0.5.2"
+sphinx = "5.0.0"
+sphinx-rtd-theme = "1.0.0"
+readthedocs-sphinx-search = "0.1.2"
+sphinx_code_tabs = "0.5.3"
+sphinx-gallery = "0.10.1"
+matplotlib = "3.9.2"
+pandas = "1.4.2"
+bert-sklearn = {git = "https://github.com/charles9n/bert-sklearn.git@master", editable = true}
+black = {version = "24.3.0", extras = ["colorama"]}
+pre-commit = "2.20.0"
+pyfakefs = "*"
+shap = "0.44.1"
+xarray = "2023.1.0"
 
 [extras]
 ray = "*"
diff --git a/Pipfile.lock b/Pipfile.lock
index 4d6c1420..2cbf59e1 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "eccfc9563430b35e4b8e92aea9e1b387fea449e1538b8162224087bad096ab71"
+            "sha256": "e04b6696c83be3a0dd3d4593c19f027b9a703a7e9e51bdb336d8eea0fd2458e6"
         },
         "pipfile-spec": 6,
         "requires": {},
@@ -9,1390 +9,2693 @@
             {
                 "name": "pypi",
                 "url": "https://pypi.python.org/simple",
-                "verify_ssl": true,
+                "verify_ssl": true
             }
-        ],
+        ]
     },
     "default": {
-        "cloudpickle": {
-            "hashes": [
-                "sha256:246ee7d0c295602a036e86369c77fecda4ab17b506496730f2f576d9016fd9c7",
-                "sha256:996d9a482c6fb4f33c1a35335cf8afd065d2a56e973270364840712d9131a882"
-            ],
-            "markers": "python_version >= '3.8'",
-            "version": "==3.0.0"
-        },
         "joblib": {
             "hashes": [
-                "sha256:92f865e621e17784e7955080b6d042489e3b8e294949cc44c6eac304f59772b1",
-                "sha256:ef4331c65f239985f3f2220ecc87db222f08fd22097a3dd5698f693875f8cbb9",
-            ],
-            "markers": "python_version >= '3.7'",
-            "version": "==1.3.2",
-        },
-        "llvmlite": {
-            "hashes": [
-                "sha256:04725975e5b2af416d685ea0769f4ecc33f97be541e301054c9f741003085802",
-                "sha256:0dd0338da625346538f1173a17cabf21d1e315cf387ca21b294ff209d176e244",
-                "sha256:150d0bc275a8ac664a705135e639178883293cf08c1a38de3bbaa2f693a0a867",
-                "sha256:1eee5cf17ec2b4198b509272cf300ee6577229d237c98cc6e63861b08463ddc6",
-                "sha256:210e458723436b2469d61b54b453474e09e12a94453c97ea3fbb0742ba5a83d8",
-                "sha256:2181bb63ef3c607e6403813421b46982c3ac6bfc1f11fa16a13eaafb46f578e6",
-                "sha256:24091a6b31242bcdd56ae2dbea40007f462260bc9bdf947953acc39dffd54f8f",
-                "sha256:2b76acee82ea0e9304be6be9d4b3840208d050ea0dcad75b1635fa06e949a0ae",
-                "sha256:2d92c51e6e9394d503033ffe3292f5bef1566ab73029ec853861f60ad5c925d0",
-                "sha256:5940bc901fb0325970415dbede82c0b7f3e35c2d5fd1d5e0047134c2c46b3281",
-                "sha256:8454c1133ef701e8c050a59edd85d238ee18bb9a0eb95faf2fca8b909ee3c89a",
-                "sha256:855f280e781d49e0640aef4c4af586831ade8f1a6c4df483fb901cbe1a48d127",
-                "sha256:880cb57ca49e862e1cd077104375b9d1dfdc0622596dfa22105f470d7bacb309",
-                "sha256:8b0a9a47c28f67a269bb62f6256e63cef28d3c5f13cbae4fab587c3ad506778b",
-                "sha256:92c32356f669e036eb01016e883b22add883c60739bc1ebee3a1cc0249a50828",
-                "sha256:92f093986ab92e71c9ffe334c002f96defc7986efda18397d0f08534f3ebdc4d",
-                "sha256:9564c19b31a0434f01d2025b06b44c7ed422f51e719ab5d24ff03b7560066c9a",
-                "sha256:b67340c62c93a11fae482910dc29163a50dff3dfa88bc874872d28ee604a83be",
-                "sha256:bf14aa0eb22b58c231243dccf7e7f42f7beec48970f2549b3a6acc737d1a4ba4",
-                "sha256:c1e1029d47ee66d3a0c4d6088641882f75b93db82bd0e6178f7bd744ebce42b9",
-                "sha256:df75594e5a4702b032684d5481db3af990b69c249ccb1d32687b8501f0689432",
-                "sha256:f19f767a018e6ec89608e1f6b13348fa2fcde657151137cb64e56d48598a92db",
-                "sha256:f8afdfa6da33f0b4226af8e64cfc2b28986e005528fbf944d0a24a72acfc9432",
-                "sha256:fa1469901a2e100c17eb8fe2678e34bd4255a3576d1a543421356e9c14d6e2ae"
+                "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6",
+                "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==0.41.1"
+            "version": "==1.4.2"
         },
         "networkx": {
             "hashes": [
-                "sha256:4f33f68cb2afcf86f28a45f43efc27a9386b535d567d2127f8f61d51dec58d36",
-                "sha256:de346335408f84de0eada6ff9fafafff9bcda11f0a0dfaa931133debb146ab61",
+                "sha256:0c127d8b2f4865f59ae9cb8aafcd60b5c70f3241ebd66f7defad7c4ab90126c9",
+                "sha256:28575580c6ebdaf4505b22c6256a2b9de86b316dc63ba9e93abde3d78dfdbcf2"
             ],
             "index": "pypi",
-            "markers": "python_version >= '3.9'",
-            "version": "==3.2.1"
-        },
-        "numba": {
-            "hashes": [
-                "sha256:07f2fa7e7144aa6f275f27260e73ce0d808d3c62b30cff8906ad1dec12d87bbe",
-                "sha256:240e7a1ae80eb6b14061dc91263b99dc8d6af9ea45d310751b780888097c1aaa",
-                "sha256:45698b995914003f890ad839cfc909eeb9c74921849c712a05405d1a79c50f68",
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-                "sha256:4e79b6cc0d2bf064a955934a2e02bf676bc7995ab2db929dbbc62e4c16551be6",
-                "sha256:55a01e1881120e86d54efdff1be08381886fe9f04fc3006af309c602a72bc44d",
-                "sha256:5c765aef472a9406a97ea9782116335ad4f9ef5c9f93fc05fd44aab0db486954",
-                "sha256:6fe7a9d8e3bd996fbe5eac0683227ccef26cba98dae6e5cee2c1894d4b9f16c1",
-                "sha256:7bf1ddd4f7b9c2306de0384bf3854cac3edd7b4d8dffae2ec1b925e4c436233f",
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-                "sha256:a1eaa744f518bbd60e1f7ccddfb8002b3d06bd865b94a5d7eac25028efe0e0ff",
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-                "sha256:bcecd3fb9df36554b342140a4d77d938a549be635d64caf8bd9ef6c47a47f8aa",
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-                "sha256:bf68df9c307fb0aa81cacd33faccd6e419496fdc621e83f1efce35cdc5e79cac",
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-            ],
-            "markers": "python_version >= '3.8'",
-            "version": "==3.1",
+            "markers": "python_version >= '3.10'",
+            "version": "==3.3"
         },
         "numpy": {
             "hashes": [
-                "sha256:0d60fbae8e0019865fc4784745814cff1c421df5afee233db6d88ab4f14655a2",
-                "sha256:1a1329e26f46230bf77b02cc19e900db9b52f398d6722ca853349a782d4cff55",
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+                "sha256:4c66707fabe114439db9068ee468c26bbdf909cac0fb58686a42a24de1760c71",
+                "sha256:50193e430acfc1346175fcbdaa28ffec49947a06918b7b92130744e81e640110",
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+                "sha256:bfe25acf8b437eb2a8b2d49d443800a5f18508cd811fea3181723922a8a82b07",
+                "sha256:cd25bcecc4974d09257ffcd1f098ee778f7834c3ad767fe5db785be9a4aa9cb2",
+                "sha256:d209d8969599b27ad20994c8e41936ee0964e6da07478d6c35016bc386b66ad4",
+                "sha256:d5241e0a80d808d70546c697135da2c613f30e28251ff8307eb72ba696945764",
+                "sha256:edd8b5fe47dab091176d21bb6de568acdd906d1887a4584a15a9a96a1dca06ef",
+                "sha256:f870204a840a60da0b12273ef34f7051e98c3b5961b61b0c2c1be6dfd64fbcd3",
+                "sha256:ffa75af20b44f8dba823498024771d5ac50620e6915abac414251bd971b4529f"
             ],
             "index": "pypi",
             "markers": "python_version >= '3.9'",
-            "version": "==1.25.2",
+            "version": "==1.26.4"
         },
         "scikit-learn": {
             "hashes": [
-                "sha256:0e8102d5036e28d08ab47166b48c8d5e5810704daecf3a476a4282d562be9a28",
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-                "sha256:fd6e2d7389542eae01077a1ee0318c4fec20c66c957f45c7aac0c6eb0fe3c612",
+                "sha256:1d0b25d9c651fd050555aadd57431b53d4cf664e749069da77f3d52c5ad14b3b",
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+                "sha256:426d258fddac674fdf33f3cb2d54d26f49406e2599dbf9a32b4d1696091d4256",
+                "sha256:44c62f2b124848a28fd695db5bc4da019287abf390bfce602ddc8aa1ec186aae",
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 }
diff --git a/docs/examples/plot_calibration.py b/docs/examples/plot_calibration.py
new file mode 100644
index 00000000..fef30968
--- /dev/null
+++ b/docs/examples/plot_calibration.py
@@ -0,0 +1,181 @@
+# -*- coding: utf-8 -*-
+"""
+=====================
+Calibrating a Classifier
+=====================
+
+A minimalist example showing how to calibrate a HiClass LCN model. The calibration method can be selected with the :literal:`calibration_method` parameter, for example:
+
+.. tabs::
+
+    .. code-tab:: python
+        :caption: Isotonic Regression
+
+        rf = RandomForestClassifier()
+        classifier = LocalClassifierPerNode(
+            local_classifier=rf,
+            calibration_method='isotonic'
+        )
+
+    .. code-tab:: python
+        :caption: Platt scaling
+
+        rf = RandomForestClassifier()
+        classifier = LocalClassifierPerNode(
+            local_classifier=rf,
+            calibration_method='platt'
+        )
+
+    .. code-tab:: python
+        :caption: Beta scaling
+
+        rf = RandomForestClassifier()
+        classifier = LocalClassifierPerNode(
+            local_classifier=rf,
+            calibration_method='beta'
+        )
+
+    .. code-tab:: python
+        :caption: IVAP
+
+        rf = RandomForestClassifier()
+        classifier = LocalClassifierPerNode(
+            local_classifier=rf,
+            calibration_method='ivap'
+        )
+
+    .. code-tab:: python
+        :caption: CVAP
+
+        rf = RandomForestClassifier()
+        classifier = LocalClassifierPerNode(
+            local_classifier=rf,
+            calibration_method='cvap'
+        )
+
+Furthermore, probabilites of multiple levels can be aggregated by defining a probability combiner:
+
+.. tabs::
+
+    .. code-tab:: python
+        :caption: Multiply (Default)
+
+        rf = RandomForestClassifier()
+        classifier = LocalClassifierPerNode(
+            local_classifier=rf,
+            calibration_method='isotonic',
+            probability_combiner='multiply'
+        )
+
+    .. code-tab:: python
+        :caption: Geometric Mean
+
+        rf = RandomForestClassifier()
+        classifier = LocalClassifierPerNode(
+            local_classifier=rf,
+            calibration_method='isotonic',
+            probability_combiner='geometric'
+        )
+
+    .. code-tab:: python
+        :caption: Arithmetic Mean
+
+        rf = RandomForestClassifier()
+        classifier = LocalClassifierPerNode(
+            local_classifier=rf,
+            calibration_method='isotonic',
+            probability_combiner='arithmetic'
+        )
+
+    .. code-tab:: python
+        :caption: No Aggregation
+
+        rf = RandomForestClassifier()
+        classifier = LocalClassifierPerNode(
+            local_classifier=rf,
+            calibration_method='isotonic',
+            probability_combiner=None
+        )
+
+
+A hierarchical classifier can be calibrated by calling calibrate on the model or by using a Pipeline:
+
+.. tabs::
+
+    .. code-tab:: python
+        :caption: Default
+
+        rf = RandomForestClassifier()
+        classifier = LocalClassifierPerNode(
+            local_classifier=rf,
+            calibration_method='isotonic'
+        )
+
+        classifier.fit(X_train, Y_train)
+        classifier.calibrate(X_cal, Y_cal)
+        classifier.predict_proba(X_test)
+
+    .. code-tab:: python
+        :caption: Pipeline
+
+        from hiclass import Pipeline
+
+        rf = RandomForestClassifier()
+        classifier = LocalClassifierPerNode(
+            local_classifier=rf,
+            calibration_method='isotonic'
+        )
+
+        pipeline = Pipeline([
+            ('classifier', classifier),
+        ])
+
+        pipeline.fit(X_train, Y_train)
+        pipeline.calibrate(X_cal, Y_cal)
+        pipeline.predict_proba(X_test)
+
+In the code below, isotonic regression is used to calibrate the model.
+
+"""
+from sklearn.ensemble import RandomForestClassifier
+
+from hiclass import LocalClassifierPerNode
+
+# Define data
+X_train = [[1], [2], [3], [4]]
+X_test = [[4], [3], [2], [1]]
+X_cal = [[5], [6], [7], [8]]
+Y_train = [
+    ["Animal", "Mammal", "Sheep"],
+    ["Animal", "Mammal", "Cow"],
+    ["Animal", "Reptile", "Snake"],
+    ["Animal", "Reptile", "Lizard"],
+]
+
+Y_cal = [
+    ["Animal", "Mammal", "Cow"],
+    ["Animal", "Mammal", "Sheep"],
+    ["Animal", "Reptile", "Lizard"],
+    ["Animal", "Reptile", "Snake"],
+]
+
+# Use random forest classifiers for every node
+rf = RandomForestClassifier()
+
+# Use local classifier per node with isotonic regression as calibration method
+classifier = LocalClassifierPerNode(
+    local_classifier=rf, calibration_method="isotonic", probability_combiner="multiply"
+)
+
+# Train local classifier per node
+classifier.fit(X_train, Y_train)
+
+# Calibrate local classifier per node
+classifier.calibrate(X_cal, Y_cal)
+
+# Predict probabilities
+probabilities = classifier.predict_proba(X_test)
+
+# Print probabilities and labels for the last level
+print(classifier.classes_[2])
+print(probabilities)
diff --git a/docs/source/algorithms/calibration.rst b/docs/source/algorithms/calibration.rst
new file mode 100644
index 00000000..c02341ee
--- /dev/null
+++ b/docs/source/algorithms/calibration.rst
@@ -0,0 +1,110 @@
+.. _calibration-overview:
+
+===========================
+Classifier Calibration
+===========================
+HiClass provides support for probability calibration using various post-hoc calibration methods. 
+
+++++++++++++++++++++++++++
+Motivation
+++++++++++++++++++++++++++
+While many machine learning models can output uncertainty scores, these scores are known to be often poorly calibrated [1]_ [2]_. Model calibration aims to improve the quality of probabilistic forecasts by learning a transformation of the scores, using a separate dataset.
+
+++++++++++++++++++++++++++
+Methods
+++++++++++++++++++++++++++
+
+HiClass supports the following calibration methods:
+
+* Isotonic Regression [3]_
+
+* Platt Scaling [4]_
+
+* Beta Calibration [5]_
+
+* Inductive Venn-Abers Calibration [6]_
+
+* Cross Venn-Abers Calibration [6]_
+
+++++++++++++++++++++++++++
+Probability Aggregation
+++++++++++++++++++++++++++
+
+Combining probabilities over multiple levels is another method to improve probabilistic forecasts. The following methods are supported:
+
+Conditional Probability Aggregation (Multiply Aggregation)
+--------------
+Given a node hierarchy with :math:`n` levels, the probability of a node :math:`A_i`, where :math:`i` denotes the level, is calculated as:
+
+:math:`\displaystyle{\mathbb{P}(A_1 \cap A_2 \cap \ldots \cap A_i) = \mathbb{P}(A_1) \cdot \mathbb{P}(A_2 \mid A_1) \cdot \mathbb{P}(A_3 \mid A_1 \cap A_2) \cdot \ldots}`
+:math:`\displaystyle{\cdot \mathbb{P}(A_i \mid A_1 \cap A_2 \cap \ldots \cap A_{i-1})}`
+
+Arithmetic Mean Aggregation
+--------------
+:math:`\displaystyle{\mathbb{P}(A_i) = \frac{1}{i} \sum_{j=1}^{i} \mathbb{P}(A_{j})}`
+
+Geometric Mean Aggregation
+--------------
+:math:`\displaystyle{\mathbb{P}(A_i) = \exp{\left(\frac{1}{i} \sum_{j=1}^{i} \ln \mathbb{P}(A_{j})\right)}}`
+
+++++++++++++++++++++++++++
+Code sample
+++++++++++++++++++++++++++
+
+.. code-block:: python
+
+    from sklearn.ensemble import RandomForestClassifier
+
+    from hiclass import LocalClassifierPerNode
+
+    # Define data
+    X_train = [[1], [2], [3], [4]]
+    X_test = [[4], [3], [2], [1]]
+    X_cal = [[5], [6], [7], [8]]
+    Y_train = [
+        ["Animal", "Mammal", "Sheep"],
+        ["Animal", "Mammal", "Cow"],
+        ["Animal", "Reptile", "Snake"],
+        ["Animal", "Reptile", "Lizard"],
+    ]
+
+    Y_cal = [
+        ["Animal", "Mammal", "Cow"],
+        ["Animal", "Mammal", "Sheep"],
+        ["Animal", "Reptile", "Lizard"],
+        ["Animal", "Reptile", "Snake"],
+    ]
+
+    # Use random forest classifiers for every node
+    rf = RandomForestClassifier()
+
+    # Use local classifier per node with isotonic regression as calibration method
+    classifier = LocalClassifierPerNode(
+        local_classifier=rf, calibration_method="isotonic", probability_combiner="multiply"
+    )
+
+    # Train local classifier per node
+    classifier.fit(X_train, Y_train)
+
+    # Calibrate local classifier per node
+    classifier.calibrate(X_cal, Y_cal)
+
+    # Predict probabilities
+    probabilities = classifier.predict_proba(X_test)
+
+    # Print probabilities and labels for the last level
+    print(classifier.classes_[2])
+    print(probabilities)
+
+.. [1] Niculescu-Mizil, Alexandru; Caruana, Rich (2005): Predicting good probabilities with supervised learning. In: Saso Dzeroski (Hg.): Proceedings of the 22nd international conference on Machine learning - ICML '05. the 22nd international conference. Bonn, Germany, 07.08.2005 - 11.08.2005. New York, New York, USA: ACM Press, S. 625-632.
+
+.. [2] Chuan Guo; Geoff Pleiss; Yu Sun; Kilian Q. Weinberger (2017): On Calibration of Modern Neural Networks. In: Doina Precup und Yee Whye Teh (Hg.): Proceedings of the 34th International Conference on Machine Learning, Bd. 70: PMLR (Proceedings of Machine Learning Research), S. 1321-1330.
+
+.. [3] Zadrozny, Bianca; Elkan, Charles (2002): Transforming classifier scores into accurate multiclass probability estimates. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: Association for Computing Machinery (KDD ’02), S. 694-699.
+
+.. [4] Platt, John (2000): Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. In: Adv. Large Margin Classif. 10.
+
+.. [5] Kull, Meelis; Filho, Telmo Silva; Flach, Peter (2017): Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers. In: Aarti Singh und Jerry Zhu (Hg.): Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Bd. 54: PMLR (Proceedings of Machine Learning Research), S. 623-631.
+
+.. [6] Vovk, Vladimir; Petej, Ivan; Fedorova, Valentina (2015): Large-scale probabilistic predictors with and without guarantees of validity. In: C. Cortes, N. Lawrence, D. Lee, M. Sugiyama und R. Garnett (Hg.): Advances in Neural Information Processing Systems, Bd. 28: Curran Associates, Inc. 
+
diff --git a/docs/source/algorithms/index.rst b/docs/source/algorithms/index.rst
index 092a6079..0087781d 100644
--- a/docs/source/algorithms/index.rst
+++ b/docs/source/algorithms/index.rst
@@ -17,3 +17,4 @@ HiClass provides implementations for the most popular machine learning models fo
     multi_label
     metrics
     explainer
+    calibration
diff --git a/docs/source/algorithms/metrics.rst b/docs/source/algorithms/metrics.rst
index bdac5909..6d4f542d 100644
--- a/docs/source/algorithms/metrics.rst
+++ b/docs/source/algorithms/metrics.rst
@@ -3,12 +3,19 @@
 Metrics
 ====================
 
+Classification Metrics
+--------------
+
 According to [1]_, the use of flat classification metrics might not be adequate to give enough insight of which algorithm is better at classifying hierarchical data. Hence, in HiClass we implemented the metrics of hierarchical precision (hP), hierarchical recall (hR) and hierarchical F-score (hF), which are extensions of the renowned metrics of precision, recall and F-score, but tailored to the hierarchical classification scenario. These hierarchical counterparts were initially proposed by [2]_, and are defined as follows:
 
 :math:`\displaystyle{hP = \frac{\sum_i|\alpha_i\cap\beta_i|}{\sum_i|\alpha_i|}}`, :math:`\displaystyle{hR = \frac{\sum_i|\alpha_i\cap\beta_i|}{\sum_i|\beta_i|}}`, :math:`\displaystyle{hF = \frac{2 \times hP \times hR}{hP + hR}}`
 
 where :math:`\alpha_i` is the set consisting of the most specific classes predicted for test example :math:`i` and all their ancestor classes, while :math:`\beta_i` is the set containing the true most specific classes of test example :math:`i` and all their ancestors, with summations computed over all test examples.
 
+Calibration Metrics
+--------------
+
+
 .. [1] Silla, C. N., & Freitas, A. A. (2011). A survey of hierarchical classification across different application domains. Data Mining and Knowledge Discovery, 22(1), 31-72.
 
 .. [2] Kiritchenko, S., Matwin, S., Nock, R., & Famili, A. F. (2006, June). Learning and evaluation in the presence of class hierarchies: Application to text categorization. In Conference of the Canadian Society for Computational Studies of Intelligence (pp. 395-406). Springer, Berlin, Heidelberg.
diff --git a/docs/source/conf.py b/docs/source/conf.py
index aecd85fd..6bd3c591 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -23,7 +23,7 @@
 # -- Project information -----------------------------------------------------
 
 project = "hiclass"
-copyright = "2022, Fabio Malcher Miranda, Niklas Köhnecke"
+copyright = "2024, Fabio Malcher Miranda, Niklas Köhnecke"
 author = "Fabio Malcher Miranda, Niklas Köhnecke"
 
 
diff --git a/hiclass/HierarchicalClassifier.py b/hiclass/HierarchicalClassifier.py
index 23e422ab..75ec94c6 100644
--- a/hiclass/HierarchicalClassifier.py
+++ b/hiclass/HierarchicalClassifier.py
@@ -7,10 +7,18 @@
 
 import networkx as nx
 import numpy as np
+import scipy
 from joblib import Parallel, delayed
 from sklearn.base import BaseEstimator
 from sklearn.linear_model import LogisticRegression
 from sklearn.utils.validation import _check_sample_weight
+from sklearn.utils.validation import check_array, check_is_fitted
+
+from hiclass.probability_combiner import (
+    GeometricMeanCombiner,
+    ArithmeticMeanCombiner,
+    MultiplyCombiner,
+)
 
 try:
     import ray
@@ -70,6 +78,7 @@ def __init__(
         n_jobs: int = 1,
         bert: bool = False,
         classifier_abbreviation: str = "",
+        calibration_method: str = None,
         tmp_dir: str = None,
     ):
         """
@@ -96,6 +105,8 @@ def __init__(
             If True, skip scikit-learn's checks and sample_weight passing for BERT.
         classifier_abbreviation : str, default=""
             The abbreviation of the local hierarchical classifier to be displayed during logging.
+        calibration_method : {"ivap", "cvap", "platt", "isotonic", "beta"}, str, default=None
+            If set, use the desired method to calibrate probabilities returned by predict_proba().
         tmp_dir : str, default=None
             Temporary directory to persist local classifiers that are trained. If the job needs to be restarted,
             it will skip the pre-trained local classifier found in the temporary directory.
@@ -107,6 +118,7 @@ def __init__(
         self.n_jobs = n_jobs
         self.bert = bert
         self.classifier_abbreviation = classifier_abbreviation
+        self.calibration_method = calibration_method
         self.tmp_dir = tmp_dir
 
     def fit(self, X, y, sample_weight=None):
@@ -120,7 +132,7 @@ def fit(self, X, y, sample_weight=None):
         X : {array-like, sparse matrix} of shape (n_samples, n_features)
             The training input samples. Internally, its dtype will be converted
             to ``dtype=np.float32``. If a sparse matrix is provided, it will be
-            converted into a sparse ``csc_matrix``.
+            converted into a sparse ``csr_matrix``.
         y : array-like of shape (n_samples, n_levels)
             The target values, i.e., hierarchical class labels for classification.
         sample_weight : array-like of shape (n_samples,), default=None
@@ -136,7 +148,8 @@ def fit(self, X, y, sample_weight=None):
         self._fit_digraph()
 
         # Delete unnecessary variables
-        self._clean_up()
+        if not self.calibration_method == "cvap":
+            self._clean_up()
 
     def _pre_fit(self, X, y, sample_weight):
         # Check that X and y have correct shape
@@ -156,14 +169,56 @@ def _pre_fit(self, X, y, sample_weight):
             self.sample_weight_ = None
 
         self.y_ = make_leveled(self.y_)
+        # Avoids creating more columns in prediction if edges are a->b and b->c,
+        # which would generate the prediction a->b->c
+        self.y_ = self._disambiguate(self.y_)
+
+        if self.y_.ndim > 1:
+            self.max_level_dimensions_ = np.array(
+                [len(np.unique(self.y_[:, level])) for level in range(self.y_.shape[1])]
+            )
+            self.global_classes_ = [
+                np.unique(self.y_[:, level]).astype("str")
+                for level in range(self.y_.shape[1])
+            ]
+            self.global_class_to_index_mapping_ = [
+                {
+                    self.global_classes_[level][index]: index
+                    for index in range(len(self.global_classes_[level]))
+                }
+                for level in range(self.y_.shape[1])
+            ]
+        else:
+            self.max_level_dimensions_ = np.array([len(np.unique(self.y_))])
+            self.global_classes_ = [np.unique(self.y_).astype("str")]
+            self.global_class_to_index_mapping_ = [
+                {
+                    self.global_classes_[0][index]: index
+                    for index in range(len(self.global_classes_[0]))
+                }
+            ]
+
+        classes_ = [self.global_classes_[0]]
+        for level in range(1, len(self.max_level_dimensions_)):
+            classes_.append(
+                np.sort(
+                    np.unique(
+                        [
+                            label.split(self.separator_)[level]
+                            for label in self.global_classes_[level]
+                        ]
+                    )
+                )
+            )
+        self.classes_ = classes_
+        self.class_to_index_mapping_ = [
+            {local_labels[index]: index for index in range(len(local_labels))}
+            for local_labels in classes_
+        ]
 
         # Create and configure logger
         self._create_logger()
 
-        # Avoids creating more columns in prediction if edges are a->b and b->c,
-        # which would generate the prediction a->b->c
-        self._disambiguate()
-
         # Create DAG from self.y_ and store to self.hierarchy_
         self._create_digraph()
 
@@ -175,7 +230,7 @@ def _pre_fit(self, X, y, sample_weight):
         self._assert_digraph_is_dag()
 
         # If y is 1D, convert to 2D for binary policies
-        self._convert_1d_y_to_2d()
+        self.y_ = self._convert_1d_y_to_2d(self.y_)
 
         # Detect root(s) and add artificial root to DAG
         self._add_artificial_root()
@@ -183,6 +238,66 @@ def _pre_fit(self, X, y, sample_weight):
         # Initialize local classifiers in DAG
         self._initialize_local_classifiers()
 
+    def calibrate(self, X, y):
+        """
+        Fit a local calibrator per node.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The calibration input samples. Internally, its dtype will be converted
+            to ``dtype=np.float32``. If a sparse matrix is provided, it will be
+            converted into a sparse ``csr_matrix``.
+        y : array-like of shape (n_samples, n_levels)
+            The target values, i.e., hierarchical class labels for classification.
+
+        Returns
+        -------
+        self : object
+            Calibrated estimator.
+        """
+        if not self.calibration_method:
+            raise ValueError("No calibration method specified")
+
+        # check if fitted
+        check_is_fitted(self)
+
+        # Input validation
+        if not self.bert:
+            X = check_array(X, accept_sparse="csr", allow_nd=True, ensure_2d=False)
+        else:
+            X = np.array(X)
+
+        if self.calibration_method == "cvap":
+            # combine train and calibration dataset for cross validation
+            if isinstance(self.X_, scipy.sparse._csr.csr_matrix):
+                self.X_cal = scipy.sparse.vstack([self.X_, X])
+            else:
+                self.X_cal = np.vstack([self.X_, X])
+
+            y = make_leveled(y)
+            y = self._disambiguate(y)
+            y = self._convert_1d_y_to_2d(y)
+            self.y_cal = np.vstack([self.y_, y])
+        else:
+            self.X_cal = X
+            y = make_leveled(y)
+            y = self._disambiguate(y)
+            y = self._convert_1d_y_to_2d(y)
+            self.y_cal = y
+
+        self.logger_.info("Calibrating")
+
+        # Create a calibrator for each local classifier
+        self._initialize_local_calibrators()
+        self._calibrate_digraph()
+
+        self._clean_up()
+        return self
+
+    def _predict_ood():
+        pass
+
     def _create_logger(self):
         # Create logger
         self.logger_ = logging.getLogger(self.classifier_abbreviation)
@@ -204,18 +319,19 @@ def _create_logger(self):
             # Add ch to logger
             self.logger_.addHandler(ch)
 
-    def _disambiguate(self):
+    def _disambiguate(self, y):
         self.separator_ = "::HiClass::Separator::"
-        if self.y_.ndim == 2:
+        if y.ndim == 2:
             new_y = []
-            for i in range(self.y_.shape[0]):
-                row = [str(self.y_[i, 0])]
-                for j in range(1, self.y_.shape[1]):
+            for i in range(y.shape[0]):
+                row = [str(y[i, 0])]
+                for j in range(1, y.shape[1]):
                     parent = str(row[-1])
-                    child = str(self.y_[i, j])
+                    child = str(y[i, j])
                     row.append(parent + self.separator_ + child)
                 new_y.append(np.asarray(row, dtype=np.str_))
-            self.y_ = np.array(new_y)
+            return np.array(new_y)
+        return y
 
     def _create_digraph(self):
         # Create DiGraph
@@ -283,10 +399,8 @@ def _assert_digraph_is_dag(self):
             self.logger_.error("Cycle detected in graph")
             raise ValueError("Graph is not directed acyclic")
 
-    def _convert_1d_y_to_2d(self):
-        # This conversion is necessary for the binary policies
-        if self.y_.ndim == 1:
-            self.y_ = np.reshape(self.y_, (-1, 1))
+    def _convert_1d_y_to_2d(self, y):
+        return np.reshape(y, (-1, 1)) if y.ndim == 1 else y
 
     def _add_artificial_root(self):
         # Detect root(s)
@@ -309,6 +423,9 @@ def _initialize_local_classifiers(self):
         else:
             self.local_classifier_ = self.local_classifier
 
+    def _initialize_local_calibrators(self):
+        self.logger_.info("Initializing local calibrators")
+
     def _convert_to_1d(self, y):
         # Convert predictions to 1D if there is only 1 column
         if self.max_levels_ == 1:
@@ -325,39 +442,126 @@ def _remove_separator(self, y):
     def _fit_node_classifier(
         self, nodes, local_mode: bool = False, use_joblib: bool = False
     ):
+        def logging_wrapper(func, idx, node, node_length):
+            self.logger_.info(f"fitting node {idx+1}/{node_length}: {str(node)}")
+            return func(self, node)
+
         if self.n_jobs > 1:
             if _has_ray and not use_joblib:
-                ray.init(
-                    num_cpus=self.n_jobs,
-                    local_mode=local_mode,
-                    ignore_reinit_error=True,
-                )
+                if not ray.is_initialized:
+                    ray.init(
+                        num_cpus=self.n_jobs,
+                        local_mode=local_mode,
+                        ignore_reinit_error=True,
+                    )
                 lcppn = ray.put(self)
-                _parallel_fit = ray.remote(self._fit_classifier)
+                _parallel_fit = ray.remote(
+                    self._fit_classifier
+                )  # TODO: use logging wrapper
                 results = [_parallel_fit.remote(lcppn, node) for node in nodes]
                 classifiers = ray.get(results)
             else:
                 classifiers = Parallel(n_jobs=self.n_jobs)(
-                    delayed(self._fit_classifier)(self, node) for node in nodes
+                    delayed(logging_wrapper)(
+                        self._fit_classifier, idx, node, len(nodes)
+                    )
+                    for idx, node in enumerate(nodes)
                 )
 
         else:
-            classifiers = [self._fit_classifier(self, node) for node in nodes]
+            classifiers = [
+                logging_wrapper(self._fit_classifier, idx, node, len(nodes))
+                for idx, node in enumerate(nodes)
+            ]
+
         for classifier, node in zip(classifiers, nodes):
             self.hierarchy_.nodes[node]["classifier"] = classifier
 
+    def _fit_node_calibrator(
+        self, nodes, local_mode: bool = False, use_joblib: bool = False
+    ):
+        def logging_wrapper(func, idx, node, node_length):
+            self.logger_.info(f"calibrating node {idx+1}/{node_length}: {str(node)}")
+            return func(self, node)
+
+        if self.n_jobs > 1:
+            if _has_ray and not use_joblib:
+                if not ray.is_initialized:
+                    ray.init(
+                        num_cpus=self.n_jobs,
+                        local_mode=local_mode,
+                        ignore_reinit_error=True,
+                    )
+                lcppn = ray.put(self)
+                _parallel_fit = ray.remote(self._fit_calibrator)
+                results = [
+                    _parallel_fit.remote(lcppn, node) for idx, node in enumerate(nodes)
+                ]  # TODO: use logging wrapper
+                calibrators = ray.get(results)
+                ray.shutdown()
+            else:
+                calibrators = Parallel(n_jobs=self.n_jobs)(
+                    delayed(logging_wrapper)(
+                        self._fit_calibrator, idx, node, len(nodes)
+                    )
+                    for idx, node in enumerate(nodes)
+                )
+
+        else:
+            calibrators = [
+                logging_wrapper(self._fit_calibrator, idx, node, len(nodes))
+                for idx, node in enumerate(nodes)
+            ]
+
+        for calibrator, node in zip(calibrators, nodes):
+            self.hierarchy_.nodes[node]["calibrator"] = calibrator
+
     @staticmethod
     def _fit_classifier(self, node):
         raise NotImplementedError(
             "Method should be implemented in the LCPN, LCPPN or LCPL"
         )
 
+    @staticmethod
+    def _fit_calibrator(self, node):
+        raise NotImplementedError("Method should be implemented in the LCPN and LCPPN")
+
+    def _create_probability_combiner(self, name):
+        if name == "geometric":
+            return GeometricMeanCombiner(self)
+        elif name == "arithmetic":
+            return ArithmeticMeanCombiner(self)
+        elif name == "multiply":
+            return MultiplyCombiner(self)
+
     def _clean_up(self):
         self.logger_.info("Cleaning up variables that can take a lot of disk space")
-        del self.X_
-        del self.y_
-        if self.sample_weight_ is not None:
+        if hasattr(self, "X_"):
+            del self.X_
+        if hasattr(self, "y_"):
+            del self.y_
+        if hasattr(self, "sample_weight") and self.sample_weight_ is not None:
             del self.sample_weight_
+        if hasattr(self, "X_cal"):
+            del self.X_cal
+        if hasattr(self, "y_cal"):
+            del self.y_cal
+
+    def _combine_and_reorder(self, proba):
+        res = [proba[0]]
+        for level in range(1, self.max_levels_):
+            res_proba = np.zeros(
+                shape=(proba[level].shape[0], len(self.classes_[level]))
+            )
+
+            for old_label in self.global_classes_[level]:
+                old_idx = self.global_class_to_index_mapping_[level][old_label]
+                local_label = old_label.split(self.separator_)[level]
+                new_idx = self.class_to_index_mapping_[level][local_label]
+                res_proba[:, new_idx] += proba[level][:, old_idx]
+
+            res.append(res_proba)
+        return res
 
     def _fit_digraph(self, local_mode: bool = False, use_joblib: bool = False):
         raise NotImplementedError(
diff --git a/hiclass/LocalClassifierPerLevel.py b/hiclass/LocalClassifierPerLevel.py
index 907e61cf..94680ab4 100644
--- a/hiclass/LocalClassifierPerLevel.py
+++ b/hiclass/LocalClassifierPerLevel.py
@@ -16,6 +16,11 @@
 
 from hiclass.ConstantClassifier import ConstantClassifier
 from hiclass.HierarchicalClassifier import HierarchicalClassifier
+from hiclass._calibration.Calibrator import _Calibrator
+
+from hiclass.probability_combiner import (
+    init_strings as probability_combiner_init_strings,
+)
 
 try:
     import ray
@@ -52,6 +57,9 @@ def __init__(
         replace_classifiers: bool = True,
         n_jobs: int = 1,
         bert: bool = False,
+        calibration_method: str = None,
+        return_all_probabilities: bool = False,
+        probability_combiner: str = "multiply",
         tmp_dir: str = None,
     ):
         """
@@ -76,6 +84,17 @@ def __init__(
             If :code:`Ray` is installed it is used, otherwise it defaults to :code:`Joblib`.
         bert : bool, default=False
             If True, skip scikit-learn's checks and sample_weight passing for BERT.
+        calibration_method : {"ivap", "cvap", "platt", "isotonic", "beta"}, str, default=None
+            If set, use the desired method to calibrate probabilities returned by predict_proba().
+        return_all_probabilities : bool, default=False
+            If True, return probabilities for all levels. Otherwise, return only probabilities for the last level.
+        probability_combiner: {"geometric", "arithmetic", "multiply", None}, str, default="multiply"
+            Specify the rule for combining probabilities over multiple levels:
+
+            - `geometric`: Each levels probabilities are calculated by taking the geometric mean of itself and its predecessors;
+            - `arithmetic`: Each levels probabilities are calculated by taking the arithmetic mean of itself and its predecessors;
+            - `multiply`: Each levels probabilities are calculated by multiplying itself with its predecessors.
+            - `None`: No aggregation.
         tmp_dir : str, default=None
             Temporary directory to persist local classifiers that are trained. If the job needs to be restarted,
             it will skip the pre-trained local classifier found in the temporary directory.
@@ -88,8 +107,19 @@ def __init__(
             n_jobs=n_jobs,
             classifier_abbreviation="LCPL",
             bert=bert,
+            calibration_method=calibration_method,
             tmp_dir=tmp_dir,
         )
+        self.return_all_probabilities = return_all_probabilities
+        self.probability_combiner = probability_combiner
+
+        if (
+            self.probability_combiner
+            and self.probability_combiner not in probability_combiner_init_strings
+        ):
+            raise ValueError(
+                f"probability_combiner must be one of {', '.join(probability_combiner_init_strings)} or None."
+            )
 
     def fit(self, X, y, sample_weight=None):
         """
@@ -100,7 +130,7 @@ def fit(self, X, y, sample_weight=None):
         X : {array-like, sparse matrix} of shape (n_samples, n_features)
             The training input samples. Internally, its dtype will be converted
             to ``dtype=np.float32``. If a sparse matrix is provided, it will be
-            converted into a sparse ``csc_matrix``.
+            converted into a sparse ``csr_matrix``.
         y : array-like of shape (n_samples, n_levels)
             The target values, i.e., hierarchical class labels for classification.
         sample_weight : array-like of shape (n_samples,), default=None
@@ -114,6 +144,7 @@ def fit(self, X, y, sample_weight=None):
         """
         # Execute common methods necessary before fitting
         super()._pre_fit(X, y, sample_weight)
+        self.local_calibrators_ = None
 
         # Fit local classifiers in DAG
         super().fit(X, y)
@@ -172,6 +203,92 @@ def predict(self, X):
 
         return y
 
+    def predict_proba(self, X):
+        """
+        Predict class probabilities for the given data.
+
+        Hierarchical labels are returned.
+        If return_all_probabilities=True: Returns the probabilities for each level.
+        Else: Returns the probabilities for the lowest level.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input samples. Internally, its dtype will be converted
+            to ``dtype=np.float32``. If a sparse matrix is provided, it will be
+            converted into a sparse ``csr_matrix``.
+        Returns
+        -------
+        T : ndarray of shape (n_samples,n_classes) or List[ndarray(n_samples,n_classes)]
+            The predicted probabilities of the lowest levels or of all levels.
+        """
+        # Check if fit has been called
+        check_is_fitted(self)
+
+        if not self.bert:
+            X = check_array(X, accept_sparse="csr", allow_nd=True, ensure_2d=False)
+        else:
+            X = np.array(X)
+
+        if not self.calibration_method:
+            self.logger_.info(
+                "It is not recommended to use predict_proba() without calibration"
+            )
+
+        # Initialize array that holds predictions
+        y = np.empty((X.shape[0], self.max_levels_), dtype=self.dtype_)
+
+        self.logger_.info("Predicting Probability")
+
+        # Predict first level
+        classifier = self.local_classifiers_[0]
+        calibrator = (
+            self.local_calibrators_[0]
+            if hasattr(self, "self.local_calibrators_")
+            else None
+        )
+
+        # use classifier as a fallback if no calibrator is available
+        calibrator = calibrator or classifier
+        proba = calibrator.predict_proba(X)
+        y[:, 0] = calibrator.classes_[np.argmax(proba, axis=1)]
+
+        level_probability_list = [proba] + self._predict_proba_remaining_levels(X, y)
+        level_probability_list = self._combine_and_reorder(level_probability_list)
+
+        # combine probabilities
+        if self.probability_combiner:
+            probability_combiner_ = self._create_probability_combiner(
+                self.probability_combiner
+            )
+            self.logger_.info(
+                f"Combining probabilities using {type(probability_combiner_).__name__}"
+            )
+            level_probability_list = probability_combiner_.combine(
+                level_probability_list
+            )
+
+        return (
+            level_probability_list
+            if self.return_all_probabilities
+            else level_probability_list[-1]
+        )
+
+    def _predict_proba_remaining_levels(self, X, y):
+        level_probability_list = []
+        for level in range(1, y.shape[1]):
+            classifier = self.local_classifiers_[level]
+            calibrator = (
+                self.local_calibrators_[level]
+                if hasattr(self, "self.local_calibrators_")
+                else None
+            )
+            # use classifier as a fallback if no calibrator is available
+            calibrator = calibrator or classifier
+            probabilities = calibrator.predict_proba(X)
+            level_probability_list.append(probabilities)
+        return level_probability_list
+
     def _predict_remaining_levels(self, X, y):
         for level in range(1, y.shape[1]):
             classifier = self.local_classifiers_[level]
@@ -222,15 +339,28 @@ def _initialize_local_classifiers(self):
         ]
         self.masks_ = [None for _ in range(self.y_.shape[1])]
 
+    def _initialize_local_calibrators(self):
+        super()._initialize_local_calibrators()
+        self.local_calibrators_ = [
+            _Calibrator(estimator=local_classifier, method=self.calibration_method)
+            for local_classifier in self.local_classifiers_
+        ]
+
     def _fit_digraph(self, local_mode: bool = False, use_joblib: bool = False):
         self.logger_.info("Fitting local classifiers")
+
+        def logging_wrapper(func, level, separator, max_level):
+            self.logger_.info(f"fitting level {level+1}/{max_level}")
+            return func(self, level, separator)
+
         if self.n_jobs > 1:
             if _has_ray and not use_joblib:
-                ray.init(
-                    num_cpus=self.n_jobs,
-                    local_mode=local_mode,
-                    ignore_reinit_error=True,
-                )
+                if not ray.is_initialized:
+                    ray.init(
+                        num_cpus=self.n_jobs,
+                        local_mode=local_mode,
+                        ignore_reinit_error=True,
+                    )
                 lcpl = ray.put(self)
                 _parallel_fit = ray.remote(self._fit_classifier)
                 results = [
@@ -240,17 +370,74 @@ def _fit_digraph(self, local_mode: bool = False, use_joblib: bool = False):
                 classifiers = ray.get(results)
             else:
                 classifiers = Parallel(n_jobs=self.n_jobs)(
-                    delayed(self._fit_classifier)(self, level, self.separator_)
+                    delayed(logging_wrapper)(
+                        self._fit_classifier,
+                        level,
+                        self.separator_,
+                        len(self.local_classifiers_),
+                    )
                     for level in range(len(self.local_classifiers_))
                 )
         else:
             classifiers = [
-                self._fit_classifier(self, level, self.separator_)
+                logging_wrapper(
+                    self._fit_classifier,
+                    level,
+                    self.separator_,
+                    len(self.local_classifiers_),
+                )
                 for level in range(len(self.local_classifiers_))
             ]
         for level, classifier in enumerate(classifiers):
             self.local_classifiers_[level] = classifier
 
+    def _calibrate_digraph(self, local_mode: bool = False, use_joblib: bool = False):
+        self.logger_.info("Fitting local calibrators")
+
+        def logging_wrapper(func, level, separator, max_level):
+            self.logger_.info(f"calibrating level {level+1}/{max_level}")
+            return func(self, level, separator)
+
+        if self.n_jobs > 1:
+            if _has_ray and not use_joblib:
+                if not ray.is_initialized:
+                    ray.init(
+                        num_cpus=self.n_jobs,
+                        local_mode=local_mode,
+                        ignore_reinit_error=True,
+                    )
+                lcpl = ray.put(self)
+                _parallel_fit = ray.remote(self._fit_calibrator)
+                results = [
+                    _parallel_fit.remote(lcpl, level, self.separator_)
+                    for level in range(len(self.local_calibrators_))
+                ]
+                calibrators = ray.get(results)
+
+            else:
+                calibrators = Parallel(n_jobs=self.n_jobs)(
+                    delayed(logging_wrapper)(
+                        self._fit_calibrator,
+                        level,
+                        self.separator_,
+                        len(self.local_calibrators_),
+                    )
+                    for level in range(len(self.local_calibrators_))
+                )
+        else:
+            calibrators = [
+                logging_wrapper(
+                    self._fit_calibrator,
+                    level,
+                    self.separator_,
+                    len(self.local_calibrators_),
+                )
+                for level in range(len(self.local_calibrators_))
+            ]
+
+        for level, calibrator in enumerate(calibrators):
+            self.local_calibrators_[level] = calibrator
+
     @staticmethod
     def _fit_classifier(self, level, separator):
         classifier = self.local_classifiers_[level]
@@ -281,6 +468,25 @@ def _fit_classifier(self, level, separator):
         self._save_tmp(level, classifier)
         return classifier
 
+    @staticmethod
+    def _fit_calibrator(self, level, separator):
+        try:
+            calibrator = self.local_calibrators_[level]
+        except IndexError:
+            self.logger_.info("no calibrator for " + "level: " + str(level))
+            return None
+
+        X, y, _ = self._remove_empty_leaves(
+            separator, self.X_cal, self.y_cal[:, level], None
+        )
+        if len(y) == 0 or len(np.unique(y)) < 2:
+            self.logger_.info(
+                f"No calibration samples to fit calibrator for level: {str(level)}"
+            )
+            return None
+        calibrator.fit(X, y)
+        return calibrator
+
     @staticmethod
     def _remove_empty_leaves(separator, X, y, sample_weight):
         # Detect rows where leaves are not empty
diff --git a/hiclass/LocalClassifierPerNode.py b/hiclass/LocalClassifierPerNode.py
index 1382c72e..c32c0781 100644
--- a/hiclass/LocalClassifierPerNode.py
+++ b/hiclass/LocalClassifierPerNode.py
@@ -17,6 +17,13 @@
 from hiclass import BinaryPolicy
 from hiclass.ConstantClassifier import ConstantClassifier
 from hiclass.HierarchicalClassifier import HierarchicalClassifier
+from hiclass._calibration.Calibrator import _Calibrator
+
+from hiclass.probability_combiner import (
+    init_strings as probability_combiner_init_strings,
+)
+
+from hiclass._hiclass_utils import _normalize_probabilities
 
 
 class LocalClassifierPerNode(BaseEstimator, HierarchicalClassifier):
@@ -47,6 +54,9 @@ def __init__(
         replace_classifiers: bool = True,
         n_jobs: int = 1,
         bert: bool = False,
+        calibration_method: str = None,
+        return_all_probabilities: bool = False,
+        probability_combiner: str = "multiply",
         tmp_dir: str = None,
     ):
         """
@@ -82,6 +92,17 @@ def __init__(
             If :code:`Ray` is installed it is used, otherwise it defaults to :code:`Joblib`.
         bert : bool, default=False
             If True, skip scikit-learn's checks and sample_weight passing for BERT.
+        calibration_method : {"ivap", "cvap", "platt", "isotonic", "beta"}, str, default=None
+            If set, use the desired method to calibrate probabilities returned by predict_proba().
+        return_all_probabilities : bool, default=False
+            If True, return probabilities for all levels. Otherwise, return only probabilities for the last level.
+        probability_combiner: {"geometric", "arithmetic", "multiply", None}, str, default="multiply"
+            Specify the rule for combining probabilities over multiple levels:
+
+            - `geometric`: Each levels probabilities are calculated by taking the geometric mean of itself and its predecessors;
+            - `arithmetic`: Each levels probabilities are calculated by taking the arithmetic mean of itself and its predecessors;
+            - `multiply`: Each levels probabilities are calculated by multiplying itself with its predecessors.
+            - `None`: No aggregation.
         tmp_dir : str, default=None
             Temporary directory to persist local classifiers that are trained. If the job needs to be restarted,
             it will skip the pre-trained local classifier found in the temporary directory.
@@ -94,9 +115,20 @@ def __init__(
             n_jobs=n_jobs,
             classifier_abbreviation="LCPN",
             bert=bert,
+            calibration_method=calibration_method,
             tmp_dir=tmp_dir,
         )
         self.binary_policy = binary_policy
+        self.return_all_probabilities = return_all_probabilities
+        self.probability_combiner = probability_combiner
+
+        if (
+            self.probability_combiner
+            and self.probability_combiner not in probability_combiner_init_strings
+        ):
+            raise ValueError(
+                f"probability_combiner must be one of {', '.join(probability_combiner_init_strings)} or None."
+            )
 
     def fit(self, X, y, sample_weight=None):
         """
@@ -107,7 +139,7 @@ def fit(self, X, y, sample_weight=None):
         X : {array-like, sparse matrix} of shape (n_samples, n_features)
             The training input samples. Internally, its dtype will be converted
             to ``dtype=np.float32``. If a sparse matrix is provided, it will be
-            converted into a sparse ``csc_matrix``.
+            converted into a sparse ``csr_matrix``.
         y : array-like of shape (n_samples, n_levels)
             The target values, i.e., hierarchical class labels for classification.
         sample_weight : array-like of shape (n_samples,), default=None
@@ -123,7 +155,7 @@ def fit(self, X, y, sample_weight=None):
         super()._pre_fit(X, y, sample_weight)
 
         # Initialize policy
-        self._initialize_binary_policy()
+        self.binary_policy_ = self._initialize_binary_policy(calibration=False)
 
         # Fit local classifiers in DAG
         super().fit(X, y)
@@ -165,7 +197,6 @@ def predict(self, X):
 
         # Initialize array that holds predictions
         y = np.empty((X.shape[0], self.max_levels_), dtype=self.dtype_)
-
         # TODO: Add threshold to stop prediction halfway if need be
 
         bfs = nx.bfs_successors(self.hierarchy_, source=self.root_)
@@ -205,13 +236,129 @@ def predict(self, X):
 
         return y
 
-    def _initialize_binary_policy(self):
+    def predict_proba(self, X):
+        """
+        Predict class probabilities for the given data.
+
+        Hierarchical labels are returned.
+        If return_all_probabilities=True: Returns the probabilities for each level.
+        Else: Returns the probabilities for the lowest level.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input samples. Internally, its dtype will be converted
+            to ``dtype=np.float32``. If a sparse matrix is provided, it will be
+            converted into a sparse ``csr_matrix``.
+        Returns
+        -------
+        T : ndarray of shape (n_samples,n_classes) or List[ndarray(n_samples,n_classes)]
+            The predicted probabilities of the lowest levels or of all levels.
+        """
+        # Check if fit has been called
+        check_is_fitted(self)
+
+        # Input validation
+        if not self.bert:
+            X = check_array(X, accept_sparse="csr", allow_nd=True, ensure_2d=False)
+        else:
+            X = np.array(X)
+
+        if not self.calibration_method:
+            self.logger_.info(
+                "It is not recommended to use predict_proba() without calibration"
+            )
+        bfs = nx.bfs_successors(self.hierarchy_, source=self.root_)
+        self.logger_.info("Predicting Probability")
+
+        y = np.empty((X.shape[0], self.max_levels_), dtype=self.dtype_)
+        level_probability_list = []
+        last_level = -1
+
+        for predecessor, successors in bfs:
+            level = nx.shortest_path_length(self.hierarchy_, self.root_, predecessor)
+            level_dimension = self.max_level_dimensions_[level]
+
+            if last_level != level:
+                last_level = level
+                cur_level_probabilities = np.zeros((X.shape[0], level_dimension))
+                level_probability_list.append(cur_level_probabilities)
+
+            if predecessor == self.root_:
+                mask = [True] * X.shape[0]
+                subset_x = X[mask]
+            else:
+                mask = np.isin(y, self.global_classes_[level - 1]).any(axis=1)
+                subset_x = X[mask]
+
+            if subset_x.shape[0] > 0:
+                local_probabilities = np.zeros((subset_x.shape[0], len(successors)))
+                for i, successor in enumerate(successors):
+                    self.logger_.info(
+                        f"Predicting probabilities for node '{str(successor)}'"
+                    )
+                    classifier = self.hierarchy_.nodes[successor]["classifier"]
+                    # use classifier as a fallback if no calibrator is available
+                    calibrator = (
+                        self.hierarchy_.nodes[successor].get("calibrator", classifier)
+                        or classifier
+                    )
+                    positive_index = np.where(calibrator.classes_ == 1)[0]
+                    proba = calibrator.predict_proba(subset_x)[:, positive_index][:, 0]
+                    local_probabilities[:, i] = proba
+                    class_index = self.global_class_to_index_mapping_[level][
+                        str(successor)
+                    ]
+                    level_probability_list[-1][mask, class_index] = proba
+
+                highest_local_probability = np.argmax(local_probabilities, axis=1)
+                path_prediction = []
+                for i in highest_local_probability:
+                    path_prediction.append(successors[i])
+                path_prediction = np.array(path_prediction)
+
+                y[mask, level] = path_prediction
+
+        y = self._convert_to_1d(y)
+        self._remove_separator(y)
+
+        # normalize probabilities
+        level_probability_list = _normalize_probabilities(level_probability_list)
+
+        # combine probabilities horizontally
+        level_probability_list = self._combine_and_reorder(level_probability_list)
+
+        # combine probabilities vertically
+        if self.probability_combiner:
+            probability_combiner_ = self._create_probability_combiner(
+                self.probability_combiner
+            )
+            self.logger_.info(
+                f"Combining probabilities using {type(probability_combiner_).__name__}"
+            )
+            level_probability_list = probability_combiner_.combine(
+                level_probability_list
+            )
+
+        return (
+            level_probability_list
+            if self.return_all_probabilities
+            else level_probability_list[-1]
+        )
+
+    def _initialize_binary_policy(self, calibration=False):
         if isinstance(self.binary_policy, str):
             self.logger_.info(f"Initializing {self.binary_policy} binary policy")
             try:
-                self.binary_policy_ = BinaryPolicy.IMPLEMENTED_POLICIES[
-                    self.binary_policy.lower()
-                ](self.hierarchy_, self.X_, self.y_, self.sample_weight_)
+                if calibration:
+                    binary_policy_ = BinaryPolicy.IMPLEMENTED_POLICIES[
+                        self.binary_policy.lower()
+                    ](self.hierarchy_, self.X_cal, self.y_cal, None)
+                else:
+                    binary_policy_ = BinaryPolicy.IMPLEMENTED_POLICIES[
+                        self.binary_policy.lower()
+                    ](self.hierarchy_, self.X_, self.y_, self.sample_weight_)
+                return binary_policy_
             except KeyError:
                 self.logger_.error(
                     f"Policy {self.binary_policy} not implemented. Available policies are:\n"
@@ -235,6 +382,21 @@ def _initialize_local_classifiers(self):
                 }
         nx.set_node_attributes(self.hierarchy_, local_classifiers)
 
+    def _initialize_local_calibrators(self):
+        super()._initialize_local_calibrators()
+        local_calibrators = {}
+        for node in self.hierarchy_.nodes:
+            # Skip only root node
+            if node != self.root_:
+                # get classifier from node
+                local_classifier = self.hierarchy_.nodes[node]["classifier"]
+                local_calibrators[node] = {
+                    "calibrator": _Calibrator(
+                        estimator=local_classifier, method=self.calibration_method
+                    )
+                }
+        nx.set_node_attributes(self.hierarchy_, local_calibrators)
+
     def _fit_digraph(self, local_mode: bool = False, use_joblib: bool = False):
         self.logger_.info("Fitting local classifiers")
         nodes = list(self.hierarchy_.nodes)
@@ -242,6 +404,14 @@ def _fit_digraph(self, local_mode: bool = False, use_joblib: bool = False):
         nodes.remove(self.root_)
         self._fit_node_classifier(nodes, local_mode, use_joblib)
 
+    def _calibrate_digraph(self, local_mode: bool = False, use_joblib: bool = False):
+        self.logger_.info("Fitting local calibrators")
+        self.cal_binary_policy_ = self._initialize_binary_policy(calibration=True)
+        nodes = list(self.hierarchy_.nodes)
+        # Remove root because it does not need to be fitted
+        nodes.remove(self.root_)
+        self._fit_node_calibrator(nodes, local_mode, use_joblib)
+
     @staticmethod
     def _fit_classifier(self, node):
         classifier = self.hierarchy_.nodes[node]["classifier"]
@@ -258,6 +428,7 @@ def _fit_classifier(self, node):
         X, y, sample_weight = self.binary_policy_.get_binary_examples(node)
         unique_y = np.unique(y)
         if len(unique_y) == 1 and self.replace_classifiers:
+            self.logger_.info("adding constant classifier")
             classifier = ConstantClassifier()
         if not self.bert:
             try:
@@ -269,6 +440,25 @@ def _fit_classifier(self, node):
         self._save_tmp(node, classifier)
         return classifier
 
+    @staticmethod
+    def _fit_calibrator(self, node):
+        try:
+            calibrator = self.hierarchy_.nodes[node]["calibrator"]
+        except KeyError:
+            self.logger_.info("no calibrator for " + "node: " + str(node))
+            return None
+        X, y, _ = self.cal_binary_policy_.get_binary_examples(node)
+        if len(y) == 0 or len(np.unique(y)) < 2:
+            self.logger_.info(
+                f"No calibration samples to fit calibrator for node: {str(node)}"
+            )
+            return None
+        calibrator.fit(X, y)
+        return calibrator
+
     def _clean_up(self):
         super()._clean_up()
-        del self.binary_policy_
+        if hasattr(self, "binary_policy_"):
+            del self.binary_policy_
+        if hasattr(self, "cal_binary_policy_"):
+            del self.cal_binary_policy_
diff --git a/hiclass/LocalClassifierPerParentNode.py b/hiclass/LocalClassifierPerParentNode.py
index 47f77475..c5373922 100644
--- a/hiclass/LocalClassifierPerParentNode.py
+++ b/hiclass/LocalClassifierPerParentNode.py
@@ -16,6 +16,13 @@
 
 from hiclass.ConstantClassifier import ConstantClassifier
 from hiclass.HierarchicalClassifier import HierarchicalClassifier
+from hiclass._calibration.Calibrator import _Calibrator
+
+from hiclass.probability_combiner import (
+    init_strings as probability_combiner_init_strings,
+)
+
+from hiclass._hiclass_utils import _normalize_probabilities
 
 
 class LocalClassifierPerParentNode(BaseEstimator, HierarchicalClassifier):
@@ -45,6 +52,9 @@ def __init__(
         replace_classifiers: bool = True,
         n_jobs: int = 1,
         bert: bool = False,
+        calibration_method: str = None,
+        return_all_probabilities: bool = False,
+        probability_combiner: str = "multiply",
         tmp_dir: str = None,
     ):
         """
@@ -69,6 +79,17 @@ def __init__(
             If :code:`Ray` is installed it is used, otherwise it defaults to :code:`Joblib`.
         bert : bool, default=False
             If True, skip scikit-learn's checks and sample_weight passing for BERT.
+        calibration_method : {"ivap", "cvap", "platt", "isotonic", "beta"}, str, default=None
+            If set, use the desired method to calibrate probabilities returned by predict_proba().
+        return_all_probabilities : bool, default=False
+            If True, return probabilities for all levels. Otherwise, return only probabilities for the last level.
+        probability_combiner: {"geometric", "arithmetic", "multiply", None}, str, default="multiply"
+            Specify the rule for combining probabilities over multiple levels:
+
+            - `geometric`: Each levels probabilities are calculated by taking the geometric mean of itself and its predecessors;
+            - `arithmetic`: Each levels probabilities are calculated by taking the arithmetic mean of itself and its predecessors;
+            - `multiply`: Each levels probabilities are calculated by multiplying itself with its predecessors.
+            - `None`: No aggregation.
         tmp_dir : str, default=None
             Temporary directory to persist local classifiers that are trained. If the job needs to be restarted,
             it will skip the pre-trained local classifier found in the temporary directory.
@@ -81,8 +102,19 @@ def __init__(
             n_jobs=n_jobs,
             classifier_abbreviation="LCPPN",
             bert=bert,
+            calibration_method=calibration_method,
             tmp_dir=tmp_dir,
         )
+        self.return_all_probabilities = return_all_probabilities
+        self.probability_combiner = probability_combiner
+
+        if (
+            self.probability_combiner
+            and self.probability_combiner not in probability_combiner_init_strings
+        ):
+            raise ValueError(
+                f"probability_combiner must be one of {', '.join(probability_combiner_init_strings)} or None."
+            )
 
     def fit(self, X, y, sample_weight=None):
         """
@@ -93,7 +125,7 @@ def fit(self, X, y, sample_weight=None):
         X : {array-like, sparse matrix} of shape (n_samples, n_features)
             The training input samples. Internally, its dtype will be converted
             to ``dtype=np.float32``. If a sparse matrix is provided, it will be
-            converted into a sparse ``csc_matrix``.
+            converted into a sparse ``csr_matrix``.
         y : array-like of shape (n_samples, n_levels)
             The target values, i.e., hierarchical class labels for classification.
         sample_weight : array-like of shape (n_samples,), default=None
@@ -165,6 +197,121 @@ def predict(self, X):
 
         return y
 
+    def predict_proba(self, X):
+        """
+        Predict class probabilities for the given data.
+
+        Hierarchical labels are returned.
+        If return_all_probabilities=True: Returns the probabilities for each level.
+        Else: Returns the probabilities for the lowest level.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input samples. Internally, its dtype will be converted
+            to ``dtype=np.float32``. If a sparse matrix is provided, it will be
+            converted into a sparse ``csr_matrix``.
+        Returns
+        -------
+        T : ndarray of shape (n_samples,n_classes) or List[ndarray(n_samples,n_classes)]
+            The predicted probabilities of the lowest levels or of all levels.
+        """
+        # Check if fit has been called
+        check_is_fitted(self)
+
+        # Input validation
+        if not self.bert:
+            X = check_array(X, accept_sparse="csr", allow_nd=True, ensure_2d=False)
+        else:
+            X = np.array(X)
+
+        if not self.calibration_method:
+            self.logger_.info(
+                "It is not recommended to use predict_proba() without calibration"
+            )
+
+        self.logger_.info("Predicting Probability")
+
+        # Initialize array that holds predictions
+        y = np.empty((X.shape[0], self.max_levels_), dtype=self.dtype_)
+
+        # Predict first level
+        classifier = self.hierarchy_.nodes[self.root_]["classifier"]
+        # use classifier as a fallback if no calibrator is available
+        calibrator = (
+            self.hierarchy_.nodes[self.root_].get("calibrator", classifier)
+            or classifier
+        )
+        proba = calibrator.predict_proba(X)
+
+        y[:, 0] = calibrator.classes_[np.argmax(proba, axis=1)]
+        level_probability_list = [proba] + self._predict_proba_remaining_levels(X, y)
+
+        level_probability_list = self._combine_and_reorder(level_probability_list)
+
+        # combine probabilities
+        if self.probability_combiner:
+            probability_combiner_ = self._create_probability_combiner(
+                self.probability_combiner
+            )
+            self.logger_.info(
+                f"Combining probabilities using {type(probability_combiner_).__name__}"
+            )
+            level_probability_list = probability_combiner_.combine(
+                level_probability_list
+            )
+
+        return (
+            level_probability_list
+            if self.return_all_probabilities
+            else level_probability_list[-1]
+        )
+
+    def _predict_proba_remaining_levels(self, X, y):
+        level_probability_list = []
+        for level in range(1, y.shape[1]):
+            predecessors = set(y[:, level - 1])
+            predecessors.discard("")
+            level_dimension = self.max_level_dimensions_[level]
+            cur_level_probabilities = np.zeros((X.shape[0], level_dimension))
+
+            for predecessor in predecessors:
+                mask = np.isin(y[:, level - 1], self.global_classes_[level - 1])
+                predecessor_x = X[mask]
+                if predecessor_x.shape[0] > 0:
+                    successors = list(self.hierarchy_.successors(predecessor))
+                    if len(successors) > 0:
+                        classifier = self.hierarchy_.nodes[predecessor]["classifier"]
+                        # use classifier as a fallback if no calibrator is available
+                        calibrator = (
+                            self.hierarchy_.nodes[predecessor].get(
+                                "calibrator", classifier
+                            )
+                            or classifier
+                        )
+
+                        proba = calibrator.predict_proba(predecessor_x)
+                        y[mask, level] = calibrator.classes_[np.argmax(proba, axis=1)]
+
+                        for successor in successors:
+                            class_index = self.global_class_to_index_mapping_[level][
+                                str(successor)
+                            ]
+
+                            proba_index = np.where(calibrator.classes_ == successor)[0][
+                                0
+                            ]
+                            cur_level_probabilities[mask, class_index] = proba[
+                                :, proba_index
+                            ]
+
+            level_probability_list.append(cur_level_probabilities)
+
+        # normalize probabilities
+        level_probability_list = _normalize_probabilities(level_probability_list)
+
+        return level_probability_list
+
     def _predict_remaining_levels(self, X, y):
         for level in range(1, y.shape[1]):
             predecessors = set(y[:, level - 1])
@@ -186,6 +333,19 @@ def _initialize_local_classifiers(self):
             local_classifiers[node] = {"classifier": deepcopy(self.local_classifier_)}
         nx.set_node_attributes(self.hierarchy_, local_classifiers)
 
+    def _initialize_local_calibrators(self):
+        super()._initialize_local_calibrators()
+        local_calibrators = {}
+        nodes = self._get_parents()
+        for node in nodes:
+            local_classifier = self.hierarchy_.nodes[node]["classifier"]
+            local_calibrators[node] = {
+                "calibrator": _Calibrator(
+                    estimator=local_classifier, method=self.calibration_method
+                )
+            }
+        nx.set_node_attributes(self.hierarchy_, local_calibrators)
+
     def _get_parents(self):
         nodes = []
         for node in self.hierarchy_.nodes:
@@ -195,19 +355,28 @@ def _get_parents(self):
                 nodes.append(node)
         return nodes
 
-    def _get_successors(self, node):
+    def _get_successors(self, node, calibration=False):
         successors = list(self.hierarchy_.successors(node))
-        mask = np.isin(self.y_, successors).any(axis=1)
-        X = self.X_[mask]
+        mask = (
+            np.isin(self.y_cal, successors).any(axis=1)
+            if calibration
+            else np.isin(self.y_, successors).any(axis=1)
+        )
+        X = self.X_cal[mask] if calibration else self.X_[mask]
         y = []
-        for row in self.y_[mask]:
+        masked_labels = self.y_cal[mask] if calibration else self.y_[mask]
+        for row in masked_labels:
             if node == self.root_:
                 y.append(row[0])
             else:
                 y.append(row[np.where(row == node)[0][0] + 1])
         y = np.array(y)
         sample_weight = (
-            self.sample_weight_[mask] if self.sample_weight_ is not None else None
+            None
+            if calibration
+            else (
+                self.sample_weight_[mask] if self.sample_weight_ is not None else None
+            )
         )
         return X, y, sample_weight
 
@@ -239,7 +408,28 @@ def _fit_classifier(self, node):
         self._save_tmp(node, classifier)
         return classifier
 
+    @staticmethod
+    def _fit_calibrator(self, node):
+        try:
+            calibrator = self.hierarchy_.nodes[node]["calibrator"]
+        except KeyError:
+            self.logger_.info("no calibrator for " + "node: " + str(node))
+            return None
+        X, y, _ = self._get_successors(node, calibration=True)
+        if len(y) == 0 or len(np.unique(y)) < 2:
+            self.logger_.info(
+                f"No calibration samples to fit calibrator for node: {str(node)}"
+            )
+            return None
+        calibrator.fit(X, y)
+        return calibrator
+
     def _fit_digraph(self, local_mode: bool = False, use_joblib: bool = False):
         self.logger_.info("Fitting local classifiers")
         nodes = self._get_parents()
         self._fit_node_classifier(nodes, local_mode, use_joblib)
+
+    def _calibrate_digraph(self, local_mode: bool = False, use_joblib: bool = False):
+        self.logger_.info("Fitting local calibrators")
+        nodes = self._get_parents()
+        self._fit_node_calibrator(nodes, local_mode, use_joblib)
diff --git a/hiclass/Pipeline.py b/hiclass/Pipeline.py
new file mode 100644
index 00000000..a1d25ada
--- /dev/null
+++ b/hiclass/Pipeline.py
@@ -0,0 +1,18 @@
+"""Custom Pipeline class that supports the `calibrate` method."""
+
+from sklearn.pipeline import Pipeline as skPipeline
+
+
+class Pipeline(skPipeline):
+    """Custom Pipeline class that supports the `calibrate` method."""
+
+    def __init__(self, steps, **kwargs):
+        """Create Pipeline object."""
+        super().__init__(steps, **kwargs)
+
+    def calibrate(self, X, y, **params):
+        """Transform the data and apply `calibrate` with the final estimator."""
+        Xt = X
+        for _, name, transform in self._iter(with_final=False):
+            Xt = transform.transform(Xt)
+        return self.steps[-1][1].calibrate(Xt, y)
diff --git a/hiclass/__init__.py b/hiclass/__init__.py
index 43abcf7a..57778bac 100644
--- a/hiclass/__init__.py
+++ b/hiclass/__init__.py
@@ -5,6 +5,7 @@
 from .LocalClassifierPerLevel import LocalClassifierPerLevel
 from .LocalClassifierPerNode import LocalClassifierPerNode
 from .LocalClassifierPerParentNode import LocalClassifierPerParentNode
+from .Pipeline import Pipeline
 from .FlatClassifier import FlatClassifier
 from .MultiLabelLocalClassifierPerNode import MultiLabelLocalClassifierPerNode
 from .MultiLabelLocalClassifierPerParentNode import (
@@ -20,6 +21,7 @@
     "LocalClassifierPerNode",
     "LocalClassifierPerParentNode",
     "LocalClassifierPerLevel",
+    "Pipeline",
     "FlatClassifier",
     "Explainer",
     "MultiLabelLocalClassifierPerNode",
diff --git a/hiclass/_calibration/BetaCalibrator.py b/hiclass/_calibration/BetaCalibrator.py
new file mode 100644
index 00000000..00bbb6e9
--- /dev/null
+++ b/hiclass/_calibration/BetaCalibrator.py
@@ -0,0 +1,52 @@
+from hiclass._calibration.BinaryCalibrator import _BinaryCalibrator
+from sklearn.utils.validation import check_is_fitted
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+
+
+class _BetaCalibrator(_BinaryCalibrator):
+    name = "BetaCalibrator"
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.skip_calibration = False
+
+    def fit(self, y: np.ndarray, scores: np.ndarray, X: np.ndarray = None):
+        unique_labels = len(np.unique(y))
+        if unique_labels < 2:
+            self.skip_calibration = True
+            self._is_fitted = True
+            return self
+
+        scores_1 = np.log(scores)
+        # replace negative infinity with limit for log(n), n -> -inf
+        replace_negative_inf = np.log(1e-300)
+        scores_1 = np.nan_to_num(scores_1, neginf=replace_negative_inf)
+
+        scores_2 = -np.log(1 - scores)
+        # replace positive infinity with limit for log(n), n -> inf
+        replace_positive_inf = np.log(1e300)
+        scores_2 = np.nan_to_num(scores_2, posinf=replace_positive_inf)
+
+        feature_matrix = np.column_stack((scores_1, scores_2))
+
+        lr = LogisticRegression()
+        lr.fit(feature_matrix, y)
+        self.a, self.b = lr.coef_.flatten()
+        self.c = lr.intercept_[0]
+
+        self._is_fitted = True
+        return self
+
+    def predict_proba(self, scores: np.ndarray, X: np.ndarray = None):
+        check_is_fitted(self)
+        if self.skip_calibration:
+            return scores
+        return 1 / (
+            1
+            + 1
+            / (
+                np.exp(self.c)
+                * (np.power(scores, self.a) / np.power((1 - scores), self.b))
+            )
+        )
diff --git a/hiclass/_calibration/BinaryCalibrator.py b/hiclass/_calibration/BinaryCalibrator.py
new file mode 100644
index 00000000..8300c473
--- /dev/null
+++ b/hiclass/_calibration/BinaryCalibrator.py
@@ -0,0 +1,20 @@
+import abc
+import numpy as np
+
+
+class _BinaryCalibrator(abc.ABC):
+    @abc.abstractmethod
+    def fit(
+        self, y: np.ndarray, scores: np.ndarray, X: np.ndarray = None
+    ):  # pragma: no cover
+        ...
+
+    @abc.abstractmethod
+    def predict_proba(
+        self, scores: np.ndarray, X: np.ndarray = None
+    ):  # pragma: no cover
+        ...
+
+    def __sklearn_is_fitted__(self):
+        """Check fitted status and return a Boolean value."""
+        return hasattr(self, "_is_fitted") and self._is_fitted
diff --git a/hiclass/_calibration/Calibrator.py b/hiclass/_calibration/Calibrator.py
new file mode 100644
index 00000000..5bb3ab73
--- /dev/null
+++ b/hiclass/_calibration/Calibrator.py
@@ -0,0 +1,129 @@
+import numpy as np
+from sklearn.base import BaseEstimator
+from sklearn.preprocessing import LabelBinarizer
+from sklearn.preprocessing import LabelEncoder
+from hiclass._calibration.VennAbersCalibrator import (
+    _InductiveVennAbersCalibrator,
+    _CrossVennAbersCalibrator,
+)
+from hiclass._calibration.IsotonicRegression import _IsotonicRegression
+from hiclass._calibration.PlattScaling import _PlattScaling
+from hiclass._calibration.BetaCalibrator import _BetaCalibrator
+from hiclass._calibration.calibration_utils import _one_vs_rest_split
+from hiclass._hiclass_utils import _normalize_probabilities
+
+
+class _Calibrator(BaseEstimator):
+    available_methods = ["ivap", "cvap", "sigmoid", "isotonic", "beta"]
+    _multiclass_methods = ["cvap"]
+
+    def __init__(
+        self, estimator: BaseEstimator, method: str = "ivap", **method_params
+    ) -> None:
+        assert callable(getattr(estimator, "predict_proba", None))
+        self.estimator = estimator
+        self.method_params = method_params
+        # self.classes_ = self.estimator.classes_
+        self.multiclass = False
+        self.multiclass_support = method in self._multiclass_methods
+        if method not in self.available_methods:
+            raise ValueError(f"{method} is not a valid calibration method.")
+        self.method = method
+
+    def fit(self, X: np.ndarray, y: np.ndarray):
+        """
+        Fit a calibrator.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The calibration input samples. Internally, its dtype will be converted
+            to ``dtype=np.float32``. If a sparse matrix is provided, it will be
+            converted into a sparse ``csr_matrix``.
+        y : array-like of shape (n_samples, n_levels)
+            The target values, i.e., hierarchical class labels for classification.
+
+        Returns
+        -------
+        self : object
+            Calibrated estimator.
+        """
+        self.classes_ = self.estimator.classes_
+        calibration_scores = self.estimator.predict_proba(X)
+
+        if calibration_scores.shape[1] > 2:
+            self.multiclass = True
+
+        self.calibrators = []
+
+        if self.multiclass:
+            if self.multiclass_support:
+                # only cvap
+                self.label_encoder = LabelEncoder()
+                encoded_y = self.label_encoder.fit_transform(y)
+                calibrator = self._create_calibrator(self.method, self.method_params)
+                calibrator.fit(encoded_y, calibration_scores, X)
+                self.calibrators.append(calibrator)
+
+            else:
+                # do one vs rest calibration
+                score_splits, label_splits = _one_vs_rest_split(
+                    y, calibration_scores, self.estimator
+                )
+                for i in range(len(score_splits)):
+                    # create a calibrator for each split
+                    calibrator = self._create_calibrator(
+                        self.method, self.method_params
+                    )
+                    calibrator.fit(label_splits[i], score_splits[i], X)
+                    self.calibrators.append(calibrator)
+
+        else:
+            self.label_encoder = LabelEncoder()
+            encoded_y = self.label_encoder.fit_transform(y)
+            calibrator = self._create_calibrator(self.method, self.method_params)
+            calibrator.fit(encoded_y, calibration_scores[:, 1], X)
+            self.calibrators.append(calibrator)
+        self._is_fitted = True
+        return self
+
+    def predict_proba(self, X: np.ndarray):
+        test_scores = self.estimator.predict_proba(X)
+
+        if self.multiclass:
+            if self.multiclass_support:
+                # only cvap
+                return self.calibrators[0].predict_proba(test_scores)
+
+            else:
+                # one vs rest calibration
+                score_splits = [test_scores[:, i] for i in range(test_scores.shape[1])]
+
+                probabilities = np.zeros((X.shape[0], len(self.estimator.classes_)))
+                for idx, split in enumerate(score_splits):
+                    probabilities[:, idx] = self.calibrators[idx].predict_proba(split)
+
+                probabilities = _normalize_probabilities(probabilities)
+
+        else:
+            probabilities = np.zeros((X.shape[0], 2))
+            probabilities[:, 1] = self.calibrators[0].predict_proba(test_scores[:, 1])
+            probabilities[:, 0] = 1.0 - probabilities[:, 1]
+
+        return probabilities
+
+    def _create_calibrator(self, name: str, params):
+        if name == "ivap":
+            return _InductiveVennAbersCalibrator(**params)
+        elif name == "cvap":
+            return _CrossVennAbersCalibrator(self.estimator, **params)
+        elif name == "sigmoid" or name == "platt":
+            return _PlattScaling()
+        elif name == "isotonic":
+            return _IsotonicRegression(params)
+        elif name == "beta":
+            return _BetaCalibrator()
+
+    def __sklearn_is_fitted__(self):
+        """Check fitted status and return a Boolean value."""
+        return hasattr(self, "_is_fitted") and self._is_fitted
diff --git a/hiclass/_calibration/IsotonicRegression.py b/hiclass/_calibration/IsotonicRegression.py
new file mode 100644
index 00000000..f6c159b2
--- /dev/null
+++ b/hiclass/_calibration/IsotonicRegression.py
@@ -0,0 +1,23 @@
+from hiclass._calibration.BinaryCalibrator import _BinaryCalibrator
+from sklearn.isotonic import IsotonicRegression as SkLearnIR
+from sklearn.utils.validation import check_is_fitted
+import numpy as np
+
+
+class _IsotonicRegression(_BinaryCalibrator):
+    name = "IsotonicRegression"
+
+    def __init__(self, params={}) -> None:
+        self._is_fitted = False
+        if "out_of_bounds" not in params:
+            params["out_of_bounds"] = "clip"
+        self.isotonic_regression = SkLearnIR(**params)
+
+    def fit(self, y: np.ndarray, scores: np.ndarray, X: np.ndarray = None):
+        self.isotonic_regression.fit(scores, y)
+        self._is_fitted = True
+        return self
+
+    def predict_proba(self, scores: np.ndarray, X: np.ndarray = None):
+        check_is_fitted(self)
+        return self.isotonic_regression.predict(scores)
diff --git a/hiclass/_calibration/PlattScaling.py b/hiclass/_calibration/PlattScaling.py
new file mode 100644
index 00000000..60d73e8e
--- /dev/null
+++ b/hiclass/_calibration/PlattScaling.py
@@ -0,0 +1,21 @@
+from hiclass._calibration.BinaryCalibrator import _BinaryCalibrator
+from sklearn.calibration import _SigmoidCalibration
+from sklearn.utils.validation import check_is_fitted
+import numpy as np
+
+
+class _PlattScaling(_BinaryCalibrator):
+    name = "PlattScaling"
+
+    def __init__(self) -> None:
+        self._is_fitted = False
+        self.platt_scaling = _SigmoidCalibration()
+
+    def fit(self, y: np.ndarray, scores: np.ndarray, X: np.ndarray = None):
+        self.platt_scaling.fit(scores, y)
+        self._is_fitted = True
+        return self
+
+    def predict_proba(self, scores: np.ndarray, X: np.ndarray = None):
+        check_is_fitted(self)
+        return self.platt_scaling.predict(scores)
diff --git a/hiclass/_calibration/VennAbersCalibrator.py b/hiclass/_calibration/VennAbersCalibrator.py
new file mode 100644
index 00000000..31e16a7c
--- /dev/null
+++ b/hiclass/_calibration/VennAbersCalibrator.py
@@ -0,0 +1,320 @@
+import numpy as np
+from sklearn.model_selection import StratifiedKFold
+from hiclass._calibration.BinaryCalibrator import _BinaryCalibrator
+from scipy.stats import gmean
+from hiclass._calibration.calibration_utils import _one_vs_rest_split
+from collections import defaultdict
+from sklearn.utils.validation import check_is_fitted
+from sklearn.base import BaseEstimator
+from hiclass._hiclass_utils import _normalize_probabilities
+
+
+class _InductiveVennAbersCalibrator(_BinaryCalibrator):
+    name = "InductiveVennAbersCalibrator"
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def fit(self, y: np.ndarray, scores: np.ndarray, X: np.ndarray = None):
+        positive_label = 1
+        unique_labels = np.unique(y)
+        assert len(unique_labels) <= 2
+
+        y = np.where(y == positive_label, 1, 0)
+        y = y.reshape(-1)  # make sure it's a 1D array
+
+        # sort all scores s1, ..., sk in increasing order
+        order_idx = np.lexsort([y, scores])
+        ordered_calibration_scores, ordered_calibration_labels = (
+            scores[order_idx],
+            y[order_idx],
+        )
+        unique_elements, unique_idx, unique_element_counts = np.unique(
+            ordered_calibration_scores, return_index=True, return_counts=True
+        )
+        ordered_unique_calibration_scores, _ = (
+            ordered_calibration_scores[unique_idx],
+            ordered_calibration_labels[unique_idx],
+        )
+
+        self.k_distinct = len(unique_idx)
+
+        ### compute csd
+        # Count the frequencies of each s'j
+        w = dict(zip(unique_elements, unique_element_counts))
+        y = np.zeros(self.k_distinct)
+        csd_1 = np.zeros((self.k_distinct + 1, 2))
+
+        for j in range(self.k_distinct):
+            s_j = ordered_unique_calibration_scores[j]
+            matching_idx = np.where(ordered_calibration_scores == s_j)
+            matching_labels = ordered_calibration_labels[matching_idx]
+            y[j] = np.sum(matching_labels) / w[unique_elements[j]]
+
+        csd_1[1:, 0] = np.cumsum(unique_element_counts)
+        csd_1[1:, 1] = np.cumsum(y * unique_element_counts)
+
+        csd_0 = csd_1.copy()
+        csd_0 = np.append(
+            csd_0, [np.array([csd_0[-1][0] + 1, csd_0[-1][1] + 0])], axis=0
+        )
+        csd_1 = np.insert(csd_1, 0, [np.array([(-1, -1)])], axis=0)
+
+        f1_stack = self._initialize_f1_corners(csd_1)
+        f0_stack = self._initialize_f0_corners(csd_0)
+
+        self._F1 = self._compute_f1(f1_stack, csd_1)
+        self._F0 = self._compute_f0(f0_stack, csd_0)
+        self._unique_elements = unique_elements
+        self._is_fitted = True
+
+        return self
+
+    def _non_left_angle_turn(self, next_to_top, top, p_i):
+        res = np.cross((top - next_to_top), (p_i - top))
+        return res <= 0
+
+    def _non_right_angle_turn(self, next_to_top, top, p_i):
+        res = np.cross((top - next_to_top), (p_i - top))
+        return res >= 0
+
+    def _initialize_f1_corners(self, csd):
+        stack = []
+        # append P_{-1} and P_0
+        stack.append(csd[0])
+        stack.append(csd[1])
+
+        for i in range(2, len(csd)):
+            while len(stack) > 1 and self._non_left_angle_turn(
+                next_to_top=stack[-2], top=stack[-1], p_i=csd[i]
+            ):
+                stack.pop()
+            stack.append(csd[i])
+
+        return stack
+
+    def _initialize_f0_corners(self, csd):
+        stack = []
+        # append p_{k'+1}, p_{k'}
+        stack.append(csd[-1])
+        stack.append(csd[-2])
+
+        for i in range(len(csd) - 3, -1, -1):
+            while len(stack) > 1 and self._non_right_angle_turn(
+                next_to_top=stack[-2], top=stack[-1], p_i=csd[i]
+            ):
+                stack.pop()
+            stack.append(csd[i])
+        return stack
+
+    def _slope(self, top, next_to_top):
+        return (next_to_top[1] - top[1]) / (next_to_top[0] - top[0])
+
+    def _at_or_above(self, p, cur_slope, top):
+        intersection_point = (p[0], top[1] + cur_slope * (p[0] - top[0]))
+        return p[1] >= intersection_point[1]
+
+    def _compute_f1(self, prev_stack, csd):
+        F1 = np.zeros(self.k_distinct + 1)
+        stack = []
+        while prev_stack:
+            stack.append(prev_stack.pop())
+
+        for i in range(2, self.k_distinct + 2):
+            F1[i - 1] = self._slope(top=stack[-1], next_to_top=stack[-2])
+            # p_{i-1}
+            csd[i - 1] = (csd[i - 2] + csd[i]) - csd[i - 1]
+            p_temp = csd[i - 1]
+
+            if self._at_or_above(p_temp, F1[i - 1], top=stack[-1]):
+                continue
+
+            stack.pop()
+            while len(stack) > 1 and self._non_left_angle_turn(
+                p_temp, stack[-1], stack[-2]
+            ):
+                stack.pop()
+            stack.append(p_temp)
+        return F1
+
+    def _compute_f0(self, prev_stack, csd):
+        F0 = np.zeros(self.k_distinct + 1)
+        stack = []
+        while prev_stack:
+            stack.append(prev_stack.pop())
+
+        for i in range(self.k_distinct, 0, -1):
+            F0[i] = self._slope(top=stack[-1], next_to_top=stack[-2])
+            csd[i] = (csd[i - 1] + csd[i + 1]) - csd[i]
+
+            if self._at_or_above(csd[i], F0[i], top=stack[-1]):
+                continue
+            stack.pop()
+            while len(stack) > 1 and self._non_right_angle_turn(
+                csd[i], stack[-1], stack[-2]
+            ):
+                stack.pop()
+            stack.append(csd[i])
+        return F0
+
+    def predict_proba(self, scores: np.ndarray, X: np.ndarray = None):
+        check_is_fitted(self)
+        lower = np.searchsorted(self._unique_elements, scores, side="left")
+        upper = np.searchsorted(self._unique_elements[:-1], scores, side="right") + 1
+
+        p0 = self._F0[lower]
+        p1 = self._F1[upper]
+
+        return p1 / (1 - p0 + p1)
+
+    def predict_intervall(self, scores: np.ndarray):
+        lower = np.searchsorted(self._unique_elements, scores, side="left")
+        upper = np.searchsorted(self._unique_elements[:-1], scores, side="right") + 1
+        p0 = self._F0[lower]
+        p1 = self._F1[upper]
+
+        return np.array(list(zip(p0, p1)))
+
+
+class _CrossVennAbersCalibrator(_BinaryCalibrator):
+    name = "CrossVennAbersCalibrator"
+
+    def __init__(self, estimator: BaseEstimator, n_folds: int = 5) -> None:
+        self._is_fitted = False
+        self.n_folds = n_folds
+        self.estimator_type = type(estimator)
+        self.estimator_params = estimator.get_params()
+        self.estimator = estimator
+        self.multiclass = False
+        self.use_estimator_fallback = False
+        self.used_cv = True
+
+    def fit(self, y: np.ndarray, scores: np.ndarray, X: np.ndarray):
+        unique_labels = np.unique(y)
+        assert len(unique_labels) >= 2
+        self.ivaps = []
+
+        try:
+            splitter = StratifiedKFold(n_splits=self.n_folds)
+            splits_x = []
+            splits_y = []
+            for train_index, cal_index in splitter.split(X, y):
+                splits_x.append((X[train_index], X[cal_index]))
+                splits_y.append((y[train_index], y[cal_index]))
+        except ValueError:
+            splits_x, splits_y = [], []
+
+        # don't use cross validation
+        if len(splits_x) == 0 or any(
+            [
+                (len(np.unique(y_train)) < 2 or len(np.unique(y_cal)) < 2)
+                for y_train, y_cal in splits_y
+            ]
+        ):
+            self.used_cv = False
+            print("skip cv split due to lack of positive samples!")
+
+            if len(unique_labels) > 2:
+                # use one vs rest
+                score_splits, label_splits = _one_vs_rest_split(
+                    y, scores, self.estimator
+                )  # TODO use only original calibration samples
+                for i in range(len(score_splits)):
+                    # create a calibrator for each split
+                    calibrator = _InductiveVennAbersCalibrator()
+                    calibrator.fit(label_splits[i], score_splits[i])
+                    self.ivaps.append(calibrator)
+            elif len(unique_labels) == 2 and scores.ndim == 1:
+                calibrator = _InductiveVennAbersCalibrator()
+                calibrator.fit(y, scores)  # TODO use only original calibration samples
+                self.ivaps.append(calibrator)
+            else:
+                print("no fitted ivaps!")
+                self.use_estimator_fallback = True
+
+        else:
+            self.ovr_ivaps = defaultdict(list)
+            for i in range(self.n_folds):
+                X_train, X_cal = splits_x[i][0], splits_x[i][1]
+                y_train, y_cal = splits_y[i][0], splits_y[i][1]
+
+                # train underlying model with x_train and y_train
+                model = self.estimator_type()
+                model.set_params(**self.estimator_params)
+                model.fit(X_train, y_train)
+
+                # calibrate IVAP with left out dataset
+                calibration_scores = model.predict_proba(X_cal)
+
+                if calibration_scores.shape[1] > 2:
+                    self.multiclass = True
+                    # one vs rest calibration
+                    score_splits, label_splits = _one_vs_rest_split(
+                        y_cal, calibration_scores, model
+                    )
+                    for idx in range(len(score_splits)):
+                        # create a calibrator for each split
+                        calibrator = _InductiveVennAbersCalibrator()
+                        calibrator.fit(label_splits[idx], score_splits[idx])
+                        self.ovr_ivaps[idx].append(calibrator)
+
+                elif calibration_scores.shape[1] == 2 and len(np.unique(y_cal)) == 2:
+                    calibrator = _InductiveVennAbersCalibrator()
+                    calibrator.fit(y_cal, calibration_scores[:, 1])
+                    self.ivaps.append(calibrator)
+
+            if len(self.ivaps) == 0 and len(self.ovr_ivaps) == 0:
+                print("no fitted ivaps!")
+                self.use_estimator_fallback = True
+
+        self._is_fitted = True
+
+        return self
+
+    def predict_proba(self, scores: np.ndarray):
+        check_is_fitted(self)
+
+        if self.use_estimator_fallback:
+            return scores
+
+        if self.multiclass:
+            score_splits = [scores[:, i] for i in range(scores.shape[1])]
+            probabilities = np.zeros((scores.shape[0], scores.shape[1]))
+
+            if self.used_cv:
+                for idx, scores in enumerate(score_splits):
+                    res = []
+
+                    if not self.ovr_ivaps[idx]:
+                        continue
+
+                    for calibrator in self.ovr_ivaps[idx]:
+                        res.append(calibrator.predict_intervall(scores))
+
+                    res = np.array(res)
+
+                    p0 = res[:, :, 0]
+                    p1 = res[:, :, 1]
+
+                    p1_gm = gmean(p1)
+                    probabilities[:, idx] = p1_gm / (gmean(1 - p0) + p1_gm)
+
+            else:
+                for idx, scores in enumerate(score_splits):
+                    probabilities[:, idx] = self.ivaps[idx].predict_proba(scores)
+
+            # normalize
+            probabilities = _normalize_probabilities(probabilities)
+            return probabilities
+
+        else:
+            res = []
+            for calibrator in self.ivaps:
+                res.append(calibrator.predict_intervall(scores))
+
+            res = np.array(res)
+            p0 = res[:, :, 0]
+            p1 = res[:, :, 1]
+
+            p1_gm = gmean(p1)
+            return p1_gm / (gmean(1 - p0) + p1_gm)
diff --git a/hiclass/_calibration/__init__.py b/hiclass/_calibration/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/hiclass/_calibration/calibration_utils.py b/hiclass/_calibration/calibration_utils.py
new file mode 100644
index 00000000..c1ea6731
--- /dev/null
+++ b/hiclass/_calibration/calibration_utils.py
@@ -0,0 +1,16 @@
+from sklearn.preprocessing import LabelBinarizer
+from sklearn.base import BaseEstimator
+import numpy as np
+
+
+def _one_vs_rest_split(y: np.ndarray, scores: np.ndarray, estimator: BaseEstimator):
+    # binarize multiclass labels
+    label_binarizer = LabelBinarizer()
+    label_binarizer.fit(estimator.classes_)
+    binary_labels = label_binarizer.transform(y).T
+
+    # split scores into k one vs rest splits
+    score_splits = [scores[:, i] for i in range(scores.shape[1])]
+    label_splits = [binary_labels[i] for i in range(len(score_splits))]
+
+    return score_splits, label_splits
diff --git a/hiclass/_hiclass_utils.py b/hiclass/_hiclass_utils.py
new file mode 100644
index 00000000..4658973e
--- /dev/null
+++ b/hiclass/_hiclass_utils.py
@@ -0,0 +1,12 @@
+import numpy as np
+
+
+def _normalize_probabilities(proba):
+    if isinstance(proba, np.ndarray):
+        return np.nan_to_num(proba / proba.sum(axis=1, keepdims=True))
+    return [
+        np.nan_to_num(
+            level_probabilities / level_probabilities.sum(axis=1, keepdims=True)
+        )
+        for level_probabilities in proba
+    ]
diff --git a/hiclass/metrics.py b/hiclass/metrics.py
index 2e6d4051..499acd27 100644
--- a/hiclass/metrics.py
+++ b/hiclass/metrics.py
@@ -1,9 +1,13 @@
 """Helper functions to compute hierarchical evaluation metrics."""
 
+from typing import Union, List
 import numpy as np
 from sklearn.utils import check_array
+from sklearn.metrics import log_loss as sk_log_loss
+from sklearn.preprocessing import LabelEncoder
 
 from hiclass.HierarchicalClassifier import make_leveled
+from hiclass import HierarchicalClassifier
 
 
 def _validate_input(y_true, y_pred):
@@ -248,3 +252,542 @@ def _compute_macro(y_true: np.ndarray, y_pred: np.ndarray, _micro_function):
         sample_score = _micro_function(np.array([ground_truth]), np.array([prediction]))
         overall_sum = overall_sum + sample_score
     return overall_sum / len(y_true)
+
+
+def _prepare_data(
+    classifier: HierarchicalClassifier,
+    y_true: np.ndarray,
+    y_prob: np.ndarray,
+    level: int,
+    y_pred: np.ndarray = None,
+):
+    classifier_classes = np.array(classifier.classes_[level]).astype("str")
+    y_true = make_leveled(y_true)
+    y_true = classifier._disambiguate(y_true)
+    y_true = np.array(list(map(lambda x: x[level], y_true)))
+    y_true = np.array([label.split(classifier.separator_)[level] for label in y_true])
+
+    if y_pred is not None:
+        y_pred = make_leveled(y_pred)
+        y_pred = classifier._disambiguate(y_pred)
+        y_pred = np.array(list(map(lambda x: x[level], y_pred)))
+        y_pred = np.array(
+            [label.split(classifier.separator_)[level] for label in y_pred]
+        )
+
+    unique_labels = np.unique(y_true).astype("str")
+    # add labels not seen in the training process
+    new_labels = np.sort(np.union1d(unique_labels, classifier_classes))
+
+    new_y_prob = np.zeros((y_prob.shape[0], len(new_labels)), dtype=np.float32)
+    for idx, label in enumerate(new_labels):
+        if label in classifier_classes:
+            old_idx = np.where(classifier_classes == label)[0][0]
+            new_y_prob[:, idx] = y_prob[:, old_idx]
+
+    return y_true, y_pred, new_labels, new_y_prob
+
+
+_calibration_aggregations = ["average", "sum", "None"]
+
+
+def _aggregate_scores(scores: np.ndarray, agg: str):
+    if agg == "average":
+        return np.mean(scores)
+    if agg == "sum":
+        return np.sum(scores)
+    if agg is None or agg == "None":
+        return scores
+
+
+def _validate_args(agg: str, y_prob: np.ndarray, level: int):
+    if agg and agg not in _calibration_aggregations:
+        raise ValueError(f"{agg} is not a valid aggregation function.")
+    if isinstance(y_prob, list) and len(y_prob) == 0:
+        raise ValueError("y_prob is empty.")
+    if (
+        isinstance(y_prob, list) and len(y_prob) == 1 or isinstance(y_prob, np.ndarray)
+    ) and level is None:
+        raise ValueError(
+            "If y_prob is not a list of probabilities the level must be specified."
+        )
+    if isinstance(y_prob, list) and len(y_prob) == 1:
+        return y_prob[0]
+    return y_prob
+
+
+def multiclass_brier_score(
+    classifier: HierarchicalClassifier,
+    y_true: np.ndarray,
+    y_prob: Union[np.ndarray, List],
+    agg: str = "average",
+    level: int = None,
+):
+    """Compute the brier score for two or more classes.
+
+    Parameters
+    ----------
+    classifier : HierarchicalClassifier
+        The classifier used.
+    y_true : np.array of shape (n_samples, n_levels)
+        Ground truth (correct) labels.
+    y_prob : np.array of shape (n_samples, n_unique_labels_per_level) or List[np.array((n_samples, n_unique_labels_per_level))]
+        Predicted probabilities.
+    agg: {"average", "sum", None}, str, default="average"
+        This parameter determines the type of averaging performed during the computation if y_prob contains probabilities for multiple levels:
+
+        - `average`: Calculate the average brier score over all levels.
+        - `sum`: Calculate the summed brier score over all levels.
+        - None: Don't aggregate results. Returns a list of brier scores.
+    level : int, default=None
+        Specifies the level of y_prob if y_prob is not a list of numpy arrays.
+    Returns
+    -------
+    brier_score : float or List[float]
+        Brier score of predicted probabilities.
+    """
+    y_prob = _validate_args(agg, y_prob, level)
+    if isinstance(y_prob, list):
+        if level:
+            return _multiclass_brier_score(classifier, y_true, y_prob[level], level)
+        scores = []
+        for level in range(make_leveled(y_true).shape[1]):
+            scores.append(
+                _multiclass_brier_score(classifier, y_true, y_prob[level], level)
+            )
+        return _aggregate_scores(scores, agg)
+    return _multiclass_brier_score(classifier, y_true, y_prob, level)
+
+
+def log_loss(
+    classifier: HierarchicalClassifier,
+    y_true: np.ndarray,
+    y_prob: Union[np.ndarray, List],
+    agg: str = "average",
+    level: int = None,
+):
+    """Compute the log loss of predicted probabilities.
+
+    Parameters
+    ----------
+    classifier : HierarchicalClassifier
+        The classifier used.
+    y_true : np.array of shape (n_samples, n_levels)
+        Ground truth (correct) labels.
+    y_prob : np.array of shape (n_samples, n_unique_labels_per_level) or List[np.array((n_samples, n_unique_labels_per_level))]
+        Predicted probabilities.
+    agg: {"average", "sum", None}, str, default="average"
+        This parameter determines the type of averaging performed during the computation if y_prob contains probabilities for multiple levels:
+
+        - `average`: Calculate the average brier score over all levels.
+        - `sum`: Calculate the summed brier score over all levels.
+        - None: Don't aggregate results. Returns a list of brier scores.
+    level : int, default=None
+        Specifies the level of y_prob if y_prob is not a list of numpy arrays.
+    Returns
+    -------
+    log_loss : float or List[float]
+        Log loss of predicted probabilities.
+    """
+    y_prob = _validate_args(agg, y_prob, level)
+    if isinstance(y_prob, list):
+        if level:
+            return _log_loss(classifier, y_true, y_prob[level], level)
+        scores = []
+        for level in range(make_leveled(y_true).shape[1]):
+            scores.append(_log_loss(classifier, y_true, y_prob[level], level))
+        return _aggregate_scores(scores, agg)
+    return _log_loss(classifier, y_true, y_prob, level)
+
+
+def expected_calibration_error(
+    classifier: HierarchicalClassifier,
+    y_true: np.ndarray,
+    y_prob: Union[np.ndarray, List],
+    y_pred: np.ndarray,
+    n_bins: int = 10,
+    agg: str = "average",
+    level: int = None,
+):
+    """Compute the expected calibration error.
+
+    Parameters
+    ----------
+    classifier : HierarchicalClassifier
+        The classifier used.
+    y_true : np.array of shape (n_samples, n_levels)
+        Ground truth (correct) labels.
+    y_prob : np.array of shape (n_samples, n_unique_labels_per_level) or List[np.array((n_samples, n_unique_labels_per_level))]
+        Predicted probabilities.
+    y_pred : np.array of shape (n_samples, n_levels)
+        Predicted labels, as returned by a classifier.
+    n_bins : int, default=10
+        Number of bins to calculate the metric.
+    agg: {"average", "sum", None}, str, default="average"
+        This parameter determines the type of averaging performed during the computation if y_prob contains probabilities for multiple levels:
+
+        - `average`: Calculate the average brier score over all levels.
+        - `sum`: Calculate the summed brier score over all levels.
+        - None: Don't aggregate results. Returns a list of brier scores.
+    level : int, default=None
+        Specifies the level of y_prob if y_prob is not a list of numpy arrays.
+    Returns
+    -------
+    expected_calibration_error : float or List[float]
+        Expected calibration error of predicted probabilities.
+    """
+    y_prob = _validate_args(agg, y_prob, level)
+    if isinstance(y_prob, list):
+        if level:
+            return _expected_calibration_error(
+                classifier, y_true, y_prob[level], y_pred, level, n_bins
+            )
+        scores = []
+        for level in range(make_leveled(y_true).shape[1]):
+            scores.append(
+                _expected_calibration_error(
+                    classifier, y_true, y_prob[level], y_pred, level, n_bins
+                )
+            )
+        return _aggregate_scores(scores, agg)
+    return _expected_calibration_error(
+        classifier, y_true, y_prob, y_pred, level, n_bins
+    )
+
+
+def static_calibration_error(
+    classifier: HierarchicalClassifier,
+    y_true: np.ndarray,
+    y_prob: Union[np.ndarray, List],
+    y_pred: np.ndarray,
+    n_bins: int = 10,
+    agg: str = "average",
+    level: int = None,
+):
+    """Compute the static calibration error.
+
+    Parameters
+    ----------
+    classifier : HierarchicalClassifier
+        The classifier used.
+    y_true : np.array of shape (n_samples, n_levels)
+        Ground truth (correct) labels.
+    y_prob : np.array of shape (n_samples, n_unique_labels_per_level) or List[np.array((n_samples, n_unique_labels_per_level))]
+        Predicted probabilities.
+    y_pred : np.array of shape (n_samples, n_levels)
+        Predicted labels, as returned by a classifier.
+    n_bins : int, default=10
+        Number of bins to calculate the metric.
+    agg: {"average", "sum", None}, str, default="average"
+        This parameter determines the type of averaging performed during the computation if y_prob contains probabilities for multiple levels:
+
+        - `average`: Calculate the average brier score over all levels.
+        - `sum`: Calculate the summed brier score over all levels.
+        - None: Don't aggregate results. Returns a list of brier scores.
+    level : int, default=None
+        Specifies the level of y_prob if y_prob is not a list of numpy arrays.
+    Returns
+    -------
+    static_calibration_error : float or List[float]
+        Static calibration error of predicted probabilities.
+    """
+    y_prob = _validate_args(agg, y_prob, level)
+    if isinstance(y_prob, list):
+        if level:
+            return _static_calibration_error(
+                classifier, y_true, y_prob[level], y_pred, level, n_bins=n_bins
+            )
+        scores = []
+        for level in range(make_leveled(y_true).shape[1]):
+            scores.append(
+                _static_calibration_error(
+                    classifier, y_true, y_prob[level], y_pred, level, n_bins=n_bins
+                )
+            )
+        return _aggregate_scores(scores, agg)
+    return _static_calibration_error(
+        classifier, y_true, y_prob, y_pred, level, n_bins=n_bins
+    )
+
+
+def adaptive_calibration_error(
+    classifier: HierarchicalClassifier,
+    y_true: np.ndarray,
+    y_prob: Union[np.ndarray, List],
+    y_pred: np.ndarray,
+    n_ranges: int = 10,
+    agg: str = "average",
+    level: int = None,
+):
+    """Compute the adaptive calibration error.
+
+    Parameters
+    ----------
+    classifier : HierarchicalClassifier
+        The classifier used.
+    y_true : np.array of shape (n_samples, n_levels)
+        Ground truth (correct) labels.
+    y_prob : np.array of shape (n_samples, n_unique_labels_per_level) or List[np.array((n_samples, n_unique_labels_per_level))]
+        Predicted probabilities.
+    y_pred : np.array of shape (n_samples, n_levels)
+        Predicted labels, as returned by a classifier.
+    n_ranges : int, default=10
+        Number of ranges to calculate the metric.
+    agg: {"average", "sum", None}, str, default="average"
+        This parameter determines the type of averaging performed during the computation if y_prob contains probabilities for multiple levels:
+
+        - `average`: Calculate the average brier score over all levels.
+        - `sum`: Calculate the summed brier score over all levels.
+        - None: Don't aggregate results. Returns a list of brier scores.
+    level : int, default=None
+        Specifies the level of y_prob if y_prob is not a list of numpy arrays.
+    Returns
+    -------
+    adaptive_calibration_error : float or List[float]
+        Adaptive calibration error of predicted probabilities.
+    """
+    y_prob = _validate_args(agg, y_prob, level)
+    if isinstance(y_prob, list):
+        if level:
+            return _adaptive_calibration_error(
+                classifier, y_true, y_prob[level], y_pred, level, n_ranges=n_ranges
+            )
+        scores = []
+        for level in range(make_leveled(y_true).shape[1]):
+            scores.append(
+                _adaptive_calibration_error(
+                    classifier, y_true, y_prob[level], y_pred, level, n_ranges=n_ranges
+                )
+            )
+        return _aggregate_scores(scores, agg)
+    return _adaptive_calibration_error(
+        classifier, y_true, y_prob, y_pred, level, n_ranges=n_ranges
+    )
+
+
+def _multiclass_brier_score(
+    classifier: HierarchicalClassifier,
+    y_true: np.ndarray,
+    y_prob: np.ndarray,
+    level: int,
+):
+    y_true, _, labels, y_prob = _prepare_data(classifier, y_true, y_prob, level)
+    label_encoder = LabelEncoder()
+    label_encoder.fit(labels)
+    y_true_encoded = label_encoder.transform(y_true)
+    return (1 / y_prob.shape[0]) * np.sum(
+        np.sum(np.square(y_prob - np.eye(y_prob.shape[1])[y_true_encoded]), axis=1)
+    )
+
+
+def _log_loss(
+    classifier: HierarchicalClassifier,
+    y_true: np.ndarray,
+    y_prob: np.ndarray,
+    level: int,
+):
+    y_true, _, labels, y_prob = _prepare_data(classifier, y_true, y_prob, level)
+    return sk_log_loss(y_true, y_prob, labels=labels)
+
+
+def _expected_calibration_error(
+    classifier: HierarchicalClassifier,
+    y_true: np.ndarray,
+    y_prob: np.ndarray,
+    y_pred: np.ndarray,
+    level: int,
+    n_bins: int = 10,
+):
+    y_true, y_pred, labels, y_prob = _prepare_data(
+        classifier, y_true, y_prob, level, y_pred
+    )
+
+    n = len(y_true)
+    label_encoder = LabelEncoder()
+    label_encoder.fit(labels)
+
+    y_true_encoded = label_encoder.transform(y_true)
+    y_pred_encoded = label_encoder.transform(y_pred)
+
+    y_prob = np.max(y_prob, axis=1)
+    stacked = np.column_stack([y_prob, y_pred_encoded, y_true_encoded])
+
+    # calculate equally sized bins
+    _, bin_edges = np.histogram(stacked, bins=n_bins, range=(0, 1))
+    bin_indices = np.digitize(stacked, bin_edges)[:, 0]
+
+    # add bin index to each data point
+    data = np.column_stack([stacked, bin_indices])
+
+    # create bin mask
+    masks = (data[:, -1, None] == range(1, n_bins + 1)).T
+
+    # create actual bins
+    bins = [data[masks[i]] for i in range(n_bins)]
+
+    # calculate ECE
+    acc = np.zeros(n_bins)
+    conf = np.zeros(n_bins)
+    ece = 0
+    for i in range(n_bins):
+        acc[i] = (
+            1 / (bins[i].shape[0]) * np.sum((bins[i][:, 1] == bins[i][:, 2]))
+            if bins[i].shape[0] != 0
+            else 0
+        )
+        conf[i] = (
+            1 / (bins[i].shape[0]) * np.sum(bins[i][:, 0])
+            if bins[i].shape[0] != 0
+            else 0
+        )
+        ece += (
+            (bins[i].shape[0] / n) * abs(acc[i] - conf[i])
+            if bins[i].shape[0] != 0
+            else 0
+        )
+    return ece
+
+
+def _static_calibration_error(
+    classifier: HierarchicalClassifier,
+    y_true: np.ndarray,
+    y_prob: np.ndarray,
+    y_pred: np.ndarray,
+    level: int,
+    n_bins: int = 10,
+):
+    y_true, y_pred, labels, y_prob = _prepare_data(
+        classifier, y_true, y_prob, level, y_pred
+    )
+
+    n_samples, n_classes = y_prob.shape
+    assert n_classes > 2
+
+    label_encoder = LabelEncoder()
+    label_encoder.fit(labels)
+
+    y_true_encoded = label_encoder.transform(y_true)
+    y_pred_encoded = label_encoder.transform(y_pred)
+
+    class_error = np.zeros(n_classes)
+
+    for k in range(n_classes):
+        class_scores = y_prob[:, k]
+        stacked = np.column_stack([class_scores, y_pred_encoded, y_true_encoded])
+
+        # create bins
+        _, bin_edges = np.histogram(stacked, bins=n_bins, range=(0, 1))
+        bin_indices = np.digitize(stacked, bin_edges)[:, 0]
+
+        # add bin index to each data point
+        data = np.column_stack([stacked, bin_indices])
+
+        # create bin mask
+        masks = (data[:, -1, None] == range(1, n_bins + 1)).T
+
+        # create actual bins
+        bins = [data[masks[i]] for i in range(n_bins)]
+
+        # calculate per class calibration error
+        acc = np.zeros(n_bins)
+        conf = np.zeros(n_bins)
+        error = 0
+        for i in range(n_bins):
+            acc[i] = (
+                1 / (bins[i].shape[0]) * np.sum((bins[i][:, 1] == bins[i][:, 2]))
+                if bins[i].shape[0] != 0
+                else 0
+            )
+            conf[i] = (
+                1 / (bins[i].shape[0]) * np.sum(bins[i][:, 0])
+                if bins[i].shape[0] != 0
+                else 0
+            )
+            error += (
+                (bins[i].shape[0] / n_samples) * abs(acc[i] - conf[i])
+                if bins[i].shape[0] != 0
+                else 0
+            )
+
+        class_error[k] = error
+
+    return np.mean(class_error)
+
+
+def _adaptive_calibration_error(
+    classifier: HierarchicalClassifier,
+    y_true: np.ndarray,
+    y_prob: np.ndarray,
+    y_pred: np.ndarray,
+    level: int,
+    n_ranges: int = 10,
+):
+    y_true, y_pred, labels, y_prob = _prepare_data(
+        classifier, y_true, y_prob, level, y_pred
+    )
+
+    _, n_classes = y_prob.shape
+    label_encoder = LabelEncoder()
+    label_encoder.fit(labels)
+
+    y_true_encoded = label_encoder.transform(y_true)
+    y_pred_encoded = label_encoder.transform(y_pred)
+
+    class_error = np.zeros(n_classes)
+
+    for k in range(n_classes):
+        class_scores = y_prob[:, k]
+
+        # sort by score probability
+        idx = np.argsort([class_scores])[0]
+        class_scores, ordered_y_pred_labels, ordered_y_true = (
+            class_scores[idx],
+            y_pred_encoded[idx],
+            y_true_encoded[idx],
+        )
+        stacked = np.column_stack(
+            [
+                np.array(range(len(class_scores))),
+                class_scores,
+                ordered_y_pred_labels,
+                ordered_y_true,
+            ]
+        )
+
+        bin_edges = np.floor(
+            np.linspace(0, len(class_scores), n_ranges + 1, endpoint=True)
+        ).astype(int)
+        _, bin_edges = np.histogram(
+            stacked, bins=bin_edges, range=(0, len(class_scores))
+        )
+        bin_indices = np.digitize(stacked, bin_edges)[:, 0]
+
+        # add bin index to each data point
+        data = np.column_stack([stacked, bin_indices])
+
+        # create bin mask
+        masks = (data[:, -1, None] == range(1, n_ranges + 1)).T
+
+        # create actual bins
+        bins = [data[masks[i]] for i in range(n_ranges)]
+
+        # calculate per class calibration error
+        acc = np.zeros(n_ranges)
+        conf = np.zeros(n_ranges)
+        error = 0
+        for i in range(n_ranges):
+            acc[i] = (
+                1 / (bins[i].shape[0]) * np.sum((bins[i][:, 2] == bins[i][:, 3]))
+                if bins[i].shape[0] != 0
+                else 0
+            )
+            conf[i] = (
+                1 / (bins[i].shape[0]) * np.sum(bins[i][:, 1])
+                if bins[i].shape[0] != 0
+                else 0
+            )
+            error += abs(acc[i] - conf[i]) if bins[i].shape[0] != 0 else 0
+
+        class_error[k] = error
+
+    return (1 / (n_classes * n_ranges)) * np.sum(class_error)
diff --git a/hiclass/probability_combiner/ArithmeticMeanCombiner.py b/hiclass/probability_combiner/ArithmeticMeanCombiner.py
new file mode 100644
index 00000000..8679e895
--- /dev/null
+++ b/hiclass/probability_combiner/ArithmeticMeanCombiner.py
@@ -0,0 +1,46 @@
+"""Defines the ArithmeticMeanCombiner."""
+
+import numpy as np
+from hiclass.probability_combiner.ProbabilityCombiner import ProbabilityCombiner
+from typing import List
+
+
+class ArithmeticMeanCombiner(ProbabilityCombiner):
+    """Combine probabilities of multiple levels by taking their arithmetic mean."""
+
+    def combine(self, proba: List[np.ndarray]):
+        """Combine probabilities of each level with probabilities of previous levels.
+
+        Calculate the arithmetic mean of node probabilities and the probabilities of its predecessors.
+        """
+        res = [proba[0]]
+        sums = [proba[0]]
+        for level in range(1, self.classifier.max_levels_):
+            level_probs = np.zeros_like(proba[level])
+            level_sum = np.zeros_like(proba[level])
+            # find all predecessors of a node
+            predecessors = self._find_predecessors(level)
+
+            for node in predecessors.keys():
+                index = self.classifier.class_to_index_mapping_[level][node]
+                # find indices of all predecessors
+                predecessor_indices = [
+                    self.classifier.class_to_index_mapping_[level - 1][predecessor]
+                    for predecessor in predecessors[node]
+                ]
+                # combine probabilities of all predecessors
+                predecessors_combined_prob = np.sum(
+                    [
+                        sums[level - 1][:, pre_index]
+                        for pre_index in predecessor_indices
+                    ],
+                    axis=0,
+                )
+                level_sum[:, index] += (
+                    proba[level][:, index] + predecessors_combined_prob
+                )
+                level_probs[:, index] = level_sum[:, index] / (level + 1)
+
+            res.append(level_probs)
+            sums.append(level_sum)
+        return self._normalize(res) if self.normalize else res
diff --git a/hiclass/probability_combiner/GeometricMeanCombiner.py b/hiclass/probability_combiner/GeometricMeanCombiner.py
new file mode 100644
index 00000000..9f643c63
--- /dev/null
+++ b/hiclass/probability_combiner/GeometricMeanCombiner.py
@@ -0,0 +1,49 @@
+"""Defines the GeometricMeanCombiner."""
+
+import numpy as np
+from hiclass.probability_combiner.ProbabilityCombiner import ProbabilityCombiner
+from typing import List
+
+
+class GeometricMeanCombiner(ProbabilityCombiner):
+    """Combine probabilities of multiple levels by taking their geometric mean."""
+
+    def combine(self, proba: List[np.ndarray]):
+        """Combine probabilities of each level with probabilities of previous levels.
+
+        Calculate the geometric mean of node probabilities and the probabilities of its predecessors.
+        """
+        res = [proba[0]]
+        log_sum = [np.log(proba[0])]
+        for level in range(1, self.classifier.max_levels_):
+            level_probs = np.zeros_like(proba[level])
+            level_log_sum = np.zeros_like(proba[level])
+            # find all predecessors of a node
+            predecessors = self._find_predecessors(level)
+
+            for node in predecessors.keys():
+                index = self.classifier.class_to_index_mapping_[level][node]
+                # find indices of all predecessors
+                predecessor_indices = [
+                    self.classifier.class_to_index_mapping_[level - 1][predecessor]
+                    for predecessor in predecessors[node]
+                ]
+                # combine probabilities of all predecessors
+                predecessors_combined_log_prob = np.log(
+                    np.sum(
+                        [
+                            np.exp(log_sum[level - 1][:, pre_index])
+                            for pre_index in predecessor_indices
+                        ],
+                        axis=0,
+                    )
+                )
+
+                level_log_sum[:, index] += (
+                    np.log(proba[level][:, index]) + predecessors_combined_log_prob
+                )
+                level_probs[:, index] = np.exp(level_log_sum[:, index] / (level + 1))
+
+            log_sum.append(level_log_sum)
+            res.append(level_probs)
+        return self._normalize(res) if self.normalize else res
diff --git a/hiclass/probability_combiner/MultiplyCombiner.py b/hiclass/probability_combiner/MultiplyCombiner.py
new file mode 100644
index 00000000..e693f940
--- /dev/null
+++ b/hiclass/probability_combiner/MultiplyCombiner.py
@@ -0,0 +1,39 @@
+"""Defines the MultiplyCombiner."""
+
+import numpy as np
+from hiclass.probability_combiner.ProbabilityCombiner import ProbabilityCombiner
+from typing import List
+
+
+class MultiplyCombiner(ProbabilityCombiner):
+    """Combine probabilities of multiple levels by multiplication."""
+
+    def combine(self, proba: List[np.ndarray]):
+        """Combine probabilities of each level with probabilities of previous levels.
+
+        Multiply node probabilities with the probabilities of its predecessors.
+        """
+        res = [proba[0]]
+        for level in range(1, self.classifier.max_levels_):
+            level_probs = np.zeros_like(proba[level])
+            # find all predecessors of a node
+            predecessors = self._find_predecessors(level)
+
+            for node in predecessors.keys():
+                index = self.classifier.class_to_index_mapping_[level][node]
+                # find indices of all predecessors
+                predecessor_indices = [
+                    self.classifier.class_to_index_mapping_[level - 1][predecessor]
+                    for predecessor in predecessors[node]
+                ]
+                # combine probabilities of all predecessors
+                predecessors_combined_prob = np.sum(
+                    [res[level - 1][:, pre_index] for pre_index in predecessor_indices],
+                    axis=0,
+                )
+                level_probs[:, index] = (
+                    predecessors_combined_prob * proba[level][:, index]
+                )
+
+            res.append(level_probs)
+        return self._normalize(res) if self.normalize else res
diff --git a/hiclass/probability_combiner/ProbabilityCombiner.py b/hiclass/probability_combiner/ProbabilityCombiner.py
new file mode 100644
index 00000000..3ff9ccaa
--- /dev/null
+++ b/hiclass/probability_combiner/ProbabilityCombiner.py
@@ -0,0 +1,40 @@
+"""Abstract class defining the structure of a probability combiner."""
+
+import abc
+import numpy as np
+from typing import List
+from collections import defaultdict
+from networkx.exception import NetworkXError
+from hiclass import HierarchicalClassifier
+from hiclass._hiclass_utils import _normalize_probabilities
+
+
+class ProbabilityCombiner(abc.ABC):
+    """Abstract class defining the structure of a probability combiner."""
+
+    def __init__(
+        self, classifier: HierarchicalClassifier, normalize: bool = True
+    ) -> None:
+        """Initialize probability combiner object."""
+        self.classifier = classifier
+        self.normalize = normalize
+
+    @abc.abstractmethod
+    def combine(self, proba: List[np.ndarray]) -> List[np.ndarray]:
+        """Combine probabilities over multiple levels."""
+        ...
+
+    def _normalize(self, proba: List[np.ndarray]):
+        return _normalize_probabilities(proba)
+
+    def _find_predecessors(self, level: int):
+        predecessors = defaultdict(list)
+        for node in self.classifier.global_classes_[level]:
+            if self.classifier.hierarchy_.has_node(node):
+                predecessor = list(self.classifier.hierarchy_.predecessors(node))[0]
+                predecessor_name = str(predecessor).split(self.classifier.separator_)[
+                    -1
+                ]
+                node_name = str(node).split(self.classifier.separator_)[-1]
+                predecessors[node_name].append(predecessor_name)
+        return predecessors
diff --git a/hiclass/probability_combiner/__init__.py b/hiclass/probability_combiner/__init__.py
new file mode 100644
index 00000000..bf762883
--- /dev/null
+++ b/hiclass/probability_combiner/__init__.py
@@ -0,0 +1,17 @@
+"""Init the probability combiner module."""
+
+from .MultiplyCombiner import MultiplyCombiner
+from .ArithmeticMeanCombiner import ArithmeticMeanCombiner
+from .GeometricMeanCombiner import GeometricMeanCombiner
+
+__all__ = [
+    "MultiplyCombiner",
+    "ArithmeticMeanCombiner",
+    "GeometricMeanCombiner",
+]
+
+init_strings = [
+    "multiply",
+    "geometric",
+    "arithmetic",
+]
diff --git a/tests/test_HierarchicalClassifier.py b/tests/test_HierarchicalClassifier.py
index 3333cf52..6d02571b 100644
--- a/tests/test_HierarchicalClassifier.py
+++ b/tests/test_HierarchicalClassifier.py
@@ -21,7 +21,7 @@ def test_disambiguate_str(ambiguous_node_str):
     ground_truth = np.array(
         [["a", "a::HiClass::Separator::b"], ["b", "b::HiClass::Separator::c"]]
     )
-    ambiguous_node_str._disambiguate()
+    ambiguous_node_str.y_ = ambiguous_node_str._disambiguate(ambiguous_node_str.y_)
     assert_array_equal(ground_truth, ambiguous_node_str.y_)
 
 
@@ -36,7 +36,7 @@ def test_disambiguate_int(ambiguous_node_int):
     ground_truth = np.array(
         [["1", "1::HiClass::Separator::2"], ["2", "2::HiClass::Separator::3"]]
     )
-    ambiguous_node_int._disambiguate()
+    ambiguous_node_int.y_ = ambiguous_node_int._disambiguate(ambiguous_node_int.y_)
     assert_array_equal(ground_truth, ambiguous_node_int.y_)
 
 
@@ -128,7 +128,7 @@ def test_assert_digraph_is_dag(cyclic_graph):
 
 def test_convert_1d_y_to_2d(graph_1d):
     ground_truth = np.array([["a"], ["b"], ["c"], ["d"]])
-    graph_1d._convert_1d_y_to_2d()
+    graph_1d.y_ = graph_1d._convert_1d_y_to_2d(graph_1d.y_)
     assert_array_equal(ground_truth, graph_1d.y_)
 
 
diff --git a/tests/test_LocalClassifierPerLevel.py b/tests/test_LocalClassifierPerLevel.py
index 27312f85..880fc904 100644
--- a/tests/test_LocalClassifierPerLevel.py
+++ b/tests/test_LocalClassifierPerLevel.py
@@ -3,13 +3,15 @@
 import networkx as nx
 import numpy as np
 import pytest
-from numpy.testing import assert_array_equal
+from numpy.testing import assert_array_equal, assert_array_almost_equal
 from scipy.sparse import csr_matrix
 from sklearn.exceptions import NotFittedError
 from sklearn.linear_model import LogisticRegression
 from sklearn.utils.estimator_checks import parametrize_with_checks
 from sklearn.utils.validation import check_is_fitted
 from hiclass import LocalClassifierPerLevel
+from hiclass._calibration.Calibrator import _Calibrator
+from hiclass.HierarchicalClassifier import make_leveled
 
 
 @parametrize_with_checks([LocalClassifierPerLevel()])
@@ -19,10 +21,14 @@ def test_sklearn_compatible_estimator(estimator, check):
 
 @pytest.fixture
 def digraph_logistic_regression():
-    digraph = LocalClassifierPerLevel(local_classifier=LogisticRegression())
+    digraph = LocalClassifierPerLevel(
+        local_classifier=LogisticRegression(), calibration_method="ivap"
+    )
     digraph.hierarchy_ = nx.DiGraph([("a", "b"), ("a", "c")])
     digraph.y_ = np.array([["a", "b"], ["a", "c"]])
     digraph.X_ = np.array([[1, 2], [3, 4]])
+    digraph.y_cal = np.array([["a", "b"], ["a", "c"]])
+    digraph.X_cal = np.array([[1, 2], [3, 4]])
     digraph.logger_ = logging.getLogger("LCPL")
     digraph.root_ = "a"
     digraph.sample_weight_ = None
@@ -43,6 +49,13 @@ def test_initialize_local_classifiers(digraph_logistic_regression):
         )
 
 
+def test_initialize_local_calibrators(digraph_logistic_regression):
+    digraph_logistic_regression._initialize_local_classifiers()
+    digraph_logistic_regression._initialize_local_calibrators()
+    for calibrator in digraph_logistic_regression.local_calibrators_:
+        assert isinstance(calibrator, _Calibrator)
+
+
 def test_fit_digraph(digraph_logistic_regression):
     classifiers = [
         LogisticRegression(),
@@ -59,6 +72,29 @@ def test_fit_digraph(digraph_logistic_regression):
     assert 1
 
 
+def test_calibrate_digraph(digraph_logistic_regression):
+    classifiers = [
+        LogisticRegression(),
+        LogisticRegression(),
+    ]
+    digraph_logistic_regression.n_jobs = 2
+    digraph_logistic_regression.local_classifiers_ = classifiers
+    digraph_logistic_regression._fit_digraph(local_mode=True)
+
+    calibrators = [
+        _Calibrator(classifier)
+        for classifier in digraph_logistic_regression.local_classifiers_
+    ]
+    digraph_logistic_regression.local_calibrators_ = calibrators
+    digraph_logistic_regression._calibrate_digraph(local_mode=True)
+
+    try:
+        check_is_fitted(digraph_logistic_regression.local_calibrators_[1])
+    except NotFittedError as e:
+        pytest.fail(repr(e))
+    assert 1
+
+
 def test_fit_digraph_joblib_multiprocessing(digraph_logistic_regression):
     classifiers = [
         LogisticRegression(),
@@ -76,9 +112,39 @@ def test_fit_digraph_joblib_multiprocessing(digraph_logistic_regression):
     assert 1
 
 
+def test_calibrate_digraph_joblib_multiprocessing(digraph_logistic_regression):
+    classifiers = [
+        LogisticRegression(),
+        LogisticRegression(),
+    ]
+    digraph_logistic_regression.n_jobs = 2
+    digraph_logistic_regression.local_classifiers_ = classifiers
+    digraph_logistic_regression._fit_digraph(local_mode=True, use_joblib=True)
+
+    calibrators = [
+        _Calibrator(classifier)
+        for classifier in digraph_logistic_regression.local_classifiers_
+    ]
+    digraph_logistic_regression.local_calibrators_ = calibrators
+    digraph_logistic_regression._calibrate_digraph(local_mode=True, use_joblib=True)
+
+    try:
+        check_is_fitted(digraph_logistic_regression.local_calibrators_[1])
+    except NotFittedError as e:
+        pytest.fail(repr(e))
+    assert 1
+
+
 @pytest.fixture
 def fitted_logistic_regression():
-    digraph = LocalClassifierPerLevel(local_classifier=LogisticRegression())
+    digraph = LocalClassifierPerLevel(
+        local_classifier=LogisticRegression(),
+        return_all_probabilities=True,
+        calibration_method="ivap",
+        probability_combiner=None,
+    )
+
+    digraph.separator_ = "::HiClass::Separator::"
     digraph.hierarchy_ = nx.DiGraph(
         [("r", "1"), ("r", "2"), ("1", "1.1"), ("1", "1.2"), ("2", "2.1"), ("2", "2.2")]
     )
@@ -86,9 +152,44 @@ def fitted_logistic_regression():
     digraph.X_ = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
     digraph.logger_ = logging.getLogger("LCPL")
     digraph.max_levels_ = 2
+
+    # for predict_proba
+    tmp_labels = digraph._disambiguate(make_leveled(digraph.y_))
+    digraph.max_level_dimensions_ = np.array(
+        [len(np.unique(tmp_labels[:, level])) for level in range(tmp_labels.shape[1])]
+    )
+    digraph.global_classes_ = [
+        np.unique(tmp_labels[:, level]).astype("str")
+        for level in range(tmp_labels.shape[1])
+    ]
+    digraph.global_class_to_index_mapping_ = [
+        {
+            digraph.global_classes_[level][index]: index
+            for index in range(len(digraph.global_classes_[level]))
+        }
+        for level in range(tmp_labels.shape[1])
+    ]
+
+    classes_ = [digraph.global_classes_[0]]
+    for level in range(1, digraph.max_levels_):
+        classes_.append(
+            np.sort(
+                np.unique(
+                    [
+                        label.split(digraph.separator_)[level]
+                        for label in digraph.global_classes_[level]
+                    ]
+                )
+            )
+        )
+    digraph.classes_ = classes_
+    digraph.class_to_index_mapping_ = [
+        {local_labels[index]: index for index in range(len(local_labels))}
+        for local_labels in classes_
+    ]
+
     digraph.dtype_ = "<U3"
     digraph.root_ = "r"
-    digraph.separator_ = "::HiClass::Separator::"
     digraph.masks_ = [
         [True, True, True, True],
         [True, True, True, True],
@@ -117,6 +218,34 @@ def test_predict_sparse(fitted_logistic_regression):
     assert_array_equal(ground_truth, prediction)
 
 
+def test_predict_proba(fitted_logistic_regression):
+    proba = fitted_logistic_regression.predict_proba([[7, 8], [5, 6], [3, 4], [1, 2]])
+    assert len(proba) == 2
+    assert proba[0].shape == (4, 2)
+    assert proba[1].shape == (4, 4)
+    assert_array_almost_equal(
+        np.sum(proba[0], axis=1), np.ones(len(proba[0])), decimal=10
+    )
+    assert_array_almost_equal(
+        np.sum(proba[1], axis=1), np.ones(len(proba[1])), decimal=10
+    )
+
+
+def test_predict_proba_sparse(fitted_logistic_regression):
+    proba = fitted_logistic_regression.predict_proba(
+        csr_matrix([[7, 8], [5, 6], [3, 4], [1, 2]])
+    )
+    assert len(proba) == 2
+    assert proba[0].shape == (4, 2)
+    assert proba[1].shape == (4, 4)
+    assert_array_almost_equal(
+        np.sum(proba[0], axis=1), np.ones(len(proba[0])), decimal=10
+    )
+    assert_array_almost_equal(
+        np.sum(proba[1], axis=1), np.ones(len(proba[1])), decimal=10
+    )
+
+
 def test_fit_predict():
     lcpl = LocalClassifierPerLevel(local_classifier=LogisticRegression())
     x = np.array([[1, 2], [3, 4]])
@@ -133,3 +262,58 @@ def test_fit_predict():
             pytest.fail(repr(e))
     predictions = lcpl.predict(x)
     assert_array_equal(y, predictions)
+
+
+def test_fit_calibrate_predict_proba():
+    lcpl = LocalClassifierPerLevel(
+        local_classifier=LogisticRegression(),
+        return_all_probabilities=True,
+        calibration_method="ivap",
+        probability_combiner="geometric",
+    )
+
+    x = np.array([[1, 2], [3, 4]])
+    y = np.array([["a", "b"], ["b", "c"]])
+    ground_truth = np.array(
+        [["a", "b"], ["a::HiClass::Separator::b", "b::HiClass::Separator::c"]]
+    )
+    lcpl.fit(x, y)
+    lcpl.calibrate(x, y)
+    for level, classifier in enumerate(lcpl.local_classifiers_):
+        try:
+            check_is_fitted(classifier)
+            assert_array_equal(ground_truth[level], classifier.classes_)
+        except NotFittedError as e:
+            pytest.fail(repr(e))
+    predictions = lcpl.predict(x)
+    proba = lcpl.predict_proba(x)
+    assert_array_equal(y, predictions)
+
+    assert len(proba) == 2
+    assert proba[0].shape == (2, 2)
+    assert proba[1].shape == (2, 2)
+    assert_array_almost_equal(
+        np.sum(proba[0], axis=1), np.ones(len(proba[0])), decimal=10
+    )
+    assert_array_almost_equal(
+        np.sum(proba[1], axis=1), np.ones(len(proba[1])), decimal=10
+    )
+
+
+def test_fit_calibrate_predict_predict_proba_bert():
+    classifier = LocalClassifierPerLevel(
+        local_classifier=LogisticRegression(),
+        return_all_probabilities=True,
+        calibration_method="ivap",
+        probability_combiner="geometric",
+    )
+
+    classifier.logger_ = logging.getLogger("HC")
+    classifier.bert = True
+    x = [[0, 1], [2, 3]]
+    y = [["a", "b"], ["c", "d"]]
+    sample_weight = None
+    classifier.fit(x, y, sample_weight)
+    classifier.calibrate(x, y)
+    classifier.predict(x)
+    classifier.predict_proba(x)
diff --git a/tests/test_LocalClassifierPerNode.py b/tests/test_LocalClassifierPerNode.py
index fb3a0ebb..2bcdf45c 100644
--- a/tests/test_LocalClassifierPerNode.py
+++ b/tests/test_LocalClassifierPerNode.py
@@ -3,12 +3,14 @@
 import networkx as nx
 import numpy as np
 import pytest
-from numpy.testing import assert_array_equal
+from numpy.testing import assert_array_equal, assert_array_almost_equal
 from scipy.sparse import csr_matrix
 from sklearn.exceptions import NotFittedError
 from sklearn.linear_model import LogisticRegression
 from sklearn.utils.estimator_checks import parametrize_with_checks
 from sklearn.utils.validation import check_is_fitted
+from hiclass._calibration.Calibrator import _Calibrator
+from hiclass.HierarchicalClassifier import make_leveled
 
 from hiclass import LocalClassifierPerNode
 
@@ -24,15 +26,15 @@ def test_sklearn_compatible_estimator(estimator, check):
 def digraph_with_policy():
     digraph = LocalClassifierPerNode(binary_policy="exclusive")
     digraph.hierarchy_ = nx.DiGraph([("a", "b")])
-    digraph.X_ = np.array([1, 2])
-    digraph.y_ = np.array(["a", "b"])
+    digraph.X_ = np.array([[1, 2]])
+    digraph.y_ = np.array([["a", "b"]])
     digraph.logger_ = logging.getLogger("LCPN")
     digraph.sample_weight_ = None
     return digraph
 
 
 def test_initialize_binary_policy(digraph_with_policy):
-    digraph_with_policy._initialize_binary_policy()
+    digraph_with_policy.binary_policy_ = digraph_with_policy._initialize_binary_policy()
     assert isinstance(digraph_with_policy.binary_policy_, ExclusivePolicy)
 
 
@@ -47,6 +49,9 @@ def digraph_with_unknown_policy():
 
 def test_initialize_unknown_binary_policy(digraph_with_unknown_policy):
     with pytest.raises(KeyError):
+        digraph_with_unknown_policy.binary_policy_ = (
+            digraph_with_unknown_policy._initialize_binary_policy()
+        )
         digraph_with_unknown_policy._initialize_binary_policy()
 
 
@@ -66,10 +71,14 @@ def test_initialize_object_binary_policy(digraph_with_object_policy):
 
 @pytest.fixture
 def digraph_logistic_regression():
-    digraph = LocalClassifierPerNode(local_classifier=LogisticRegression())
+    digraph = LocalClassifierPerNode(
+        local_classifier=LogisticRegression(), calibration_method="ivap"
+    )
     digraph.hierarchy_ = nx.DiGraph([("a", "b"), ("a", "c")])
     digraph.y_ = np.array([["a", "b"], ["a", "c"]])
     digraph.X_ = np.array([[1, 2], [3, 4]])
+    digraph.y_cal = np.array([["a", "b"], ["a", "c"]])
+    digraph.X_cal = np.array([[1, 2], [3, 4]])
     digraph.logger_ = logging.getLogger("LCPN")
     digraph.root_ = "a"
     digraph.separator_ = "::HiClass::Separator::"
@@ -94,6 +103,24 @@ def test_initialize_local_classifiers(digraph_logistic_regression):
                 )
 
 
+def test_initialize_local_calibrators(digraph_logistic_regression):
+    digraph_logistic_regression._initialize_local_classifiers()
+    digraph_logistic_regression._initialize_local_calibrators()
+
+    for node in digraph_logistic_regression.hierarchy_.nodes:
+        if node != digraph_logistic_regression.root_:
+            assert isinstance(
+                digraph_logistic_regression.hierarchy_.nodes[node]["calibrator"],
+                _Calibrator,
+            )
+        else:
+            with pytest.raises(KeyError):
+                isinstance(
+                    digraph_logistic_regression.hierarchy_.nodes[node]["classifier"],
+                    _Calibrator,
+                )
+
+
 def test_fit_digraph(digraph_logistic_regression):
     classifiers = {
         "b": {"classifier": LogisticRegression()},
@@ -114,6 +141,42 @@ def test_fit_digraph(digraph_logistic_regression):
     assert 1
 
 
+def test_calibrate_digraph(digraph_logistic_regression):
+    classifiers = {
+        "b": {"classifier": LogisticRegression()},
+        "c": {"classifier": LogisticRegression()},
+    }
+    digraph_logistic_regression.n_jobs = 2
+    nx.set_node_attributes(digraph_logistic_regression.hierarchy_, classifiers)
+    digraph_logistic_regression._fit_digraph(local_mode=True)
+
+    calibrators = {
+        "b": {
+            "calibrator": _Calibrator(
+                digraph_logistic_regression.hierarchy_.nodes["b"]["classifier"]
+            )
+        },
+        "c": {
+            "calibrator": _Calibrator(
+                digraph_logistic_regression.hierarchy_.nodes["c"]["classifier"]
+            )
+        },
+    }
+    nx.set_node_attributes(digraph_logistic_regression.hierarchy_, calibrators)
+    digraph_logistic_regression._calibrate_digraph(local_mode=True)
+
+    with pytest.raises(KeyError):
+        check_is_fitted(digraph_logistic_regression.hierarchy_.nodes["a"]["calibrator"])
+    for node in ["b", "c"]:
+        try:
+            check_is_fitted(
+                digraph_logistic_regression.hierarchy_.nodes[node]["calibrator"]
+            )
+        except NotFittedError as e:
+            pytest.fail(repr(e))
+    assert 1
+
+
 def test_fit_digraph_joblib_multiprocessing(digraph_logistic_regression):
     classifiers = {
         "b": {"classifier": LogisticRegression()},
@@ -134,6 +197,42 @@ def test_fit_digraph_joblib_multiprocessing(digraph_logistic_regression):
     assert 1
 
 
+def test_calibrate_digraph_joblib_multiprocessing(digraph_logistic_regression):
+    classifiers = {
+        "b": {"classifier": LogisticRegression()},
+        "c": {"classifier": LogisticRegression()},
+    }
+    digraph_logistic_regression.n_jobs = 2
+    nx.set_node_attributes(digraph_logistic_regression.hierarchy_, classifiers)
+    digraph_logistic_regression._fit_digraph(local_mode=True, use_joblib=True)
+
+    calibrators = {
+        "b": {
+            "calibrator": _Calibrator(
+                digraph_logistic_regression.hierarchy_.nodes["b"]["classifier"]
+            )
+        },
+        "c": {
+            "calibrator": _Calibrator(
+                digraph_logistic_regression.hierarchy_.nodes["c"]["classifier"]
+            )
+        },
+    }
+    nx.set_node_attributes(digraph_logistic_regression.hierarchy_, calibrators)
+    digraph_logistic_regression._calibrate_digraph(local_mode=True, use_joblib=True)
+
+    with pytest.raises(KeyError):
+        check_is_fitted(digraph_logistic_regression.hierarchy_.nodes["a"]["calibrator"])
+    for node in ["b", "c"]:
+        try:
+            check_is_fitted(
+                digraph_logistic_regression.hierarchy_.nodes[node]["calibrator"]
+            )
+        except NotFittedError as e:
+            pytest.fail(repr(e))
+    assert 1
+
+
 def test_clean_up(digraph_logistic_regression):
     digraph_logistic_regression._clean_up()
     with pytest.raises(AttributeError):
@@ -142,35 +241,97 @@ def test_clean_up(digraph_logistic_regression):
         assert digraph_logistic_regression.y_ is None
     with pytest.raises(AttributeError):
         assert digraph_logistic_regression.binary_policy_ is None
+    with pytest.raises(AttributeError):
+        assert digraph_logistic_regression.cal_binary_policy_ is None
 
 
 @pytest.fixture
 def fitted_logistic_regression():
-    digraph = LocalClassifierPerNode(local_classifier=LogisticRegression())
+    digraph = LocalClassifierPerNode(
+        local_classifier=LogisticRegression(),
+        return_all_probabilities=True,
+        calibration_method="ivap",
+        probability_combiner="geometric",
+    )
+
+    digraph.separator_ = "::HiClass::Separator::"
+    # digraph.hierarchy_ = nx.DiGraph(
+    #    [("r", "1"), ("r", "2"), ("1", "1.1"), ("1", "1.2"), ("2", "2.1"), ("2", "2.2")]
+    # )
+
     digraph.hierarchy_ = nx.DiGraph(
-        [("r", "1"), ("r", "2"), ("1", "1.1"), ("1", "1.2"), ("2", "2.1"), ("2", "2.2")]
+        [
+            ("r", "1"),
+            ("r", "2"),
+            ("1", "1" + digraph.separator_ + "1.1"),
+            ("1", "1" + digraph.separator_ + "1.2"),
+            ("2", "2" + digraph.separator_ + "2.1"),
+            ("2", "2" + digraph.separator_ + "2.2"),
+        ]
     )
     digraph.y_ = np.array([["1", "1.1"], ["1", "1.2"], ["2", "2.1"], ["2", "2.2"]])
+
     digraph.X_ = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
     digraph.logger_ = logging.getLogger("LCPN")
     digraph.max_levels_ = 2
-    digraph.dtype_ = "<U3"
+    digraph.dtype_ = "<U30"
     digraph.root_ = "r"
-    digraph.separator_ = "::HiClass::Separator::"
+
+    # for predict_proba
+    tmp_labels = digraph._disambiguate(make_leveled(digraph.y_))
+    # digraph.y_ = tmp_labels
+    digraph.max_level_dimensions_ = np.array(
+        [len(np.unique(tmp_labels[:, level])) for level in range(tmp_labels.shape[1])]
+    )
+    digraph.global_classes_ = [
+        np.unique(tmp_labels[:, level]).astype("str")
+        for level in range(tmp_labels.shape[1])
+    ]
+    digraph.global_class_to_index_mapping_ = [
+        {
+            digraph.global_classes_[level][index]: index
+            for index in range(len(digraph.global_classes_[level]))
+        }
+        for level in range(tmp_labels.shape[1])
+    ]
+
+    classes_ = [digraph.global_classes_[0]]
+    for level in range(1, digraph.max_levels_):
+        classes_.append(
+            np.sort(
+                np.unique(
+                    [
+                        label.split(digraph.separator_)[level]
+                        for label in digraph.global_classes_[level]
+                    ]
+                )
+            )
+        )
+    digraph.classes_ = classes_
+    digraph.class_to_index_mapping_ = [
+        {local_labels[index]: index for index in range(len(local_labels))}
+        for local_labels in classes_
+    ]
+
     classifiers = {
-        "1": {"classifier": LogisticRegression()},
-        "1.1": {"classifier": LogisticRegression()},
-        "1.2": {"classifier": LogisticRegression()},
-        "2": {"classifier": LogisticRegression()},
-        "2.1": {"classifier": LogisticRegression()},
-        "2.2": {"classifier": LogisticRegression()},
+        key: {"classifier": LogisticRegression()}
+        for key in np.hstack([digraph.global_classes_[0], digraph.global_classes_[1]])
     }
+
     classifiers["1"]["classifier"].fit(digraph.X_, [1, 1, 0, 0])
-    classifiers["1.1"]["classifier"].fit(digraph.X_, [1, 0, 0, 0])
-    classifiers["1.2"]["classifier"].fit(digraph.X_, [0, 1, 0, 0])
+    classifiers["1" + digraph.separator_ + "1.1"]["classifier"].fit(
+        digraph.X_, [1, 0, 0, 0]
+    )
+    classifiers["1" + digraph.separator_ + "1.2"]["classifier"].fit(
+        digraph.X_, [0, 1, 0, 0]
+    )
     classifiers["2"]["classifier"].fit(digraph.X_, [0, 0, 1, 1])
-    classifiers["2.1"]["classifier"].fit(digraph.X_, [0, 0, 1, 0])
-    classifiers["2.2"]["classifier"].fit(digraph.X_, [0, 0, 0, 1])
+    classifiers["2" + digraph.separator_ + "2.1"]["classifier"].fit(
+        digraph.X_, [0, 0, 1, 0]
+    )
+    classifiers["2" + digraph.separator_ + "2.2"]["classifier"].fit(
+        digraph.X_, [0, 0, 0, 1]
+    )
     nx.set_node_attributes(digraph.hierarchy_, classifiers)
     return digraph
 
@@ -189,6 +350,34 @@ def test_predict_sparse(fitted_logistic_regression):
     assert_array_equal(ground_truth, prediction)
 
 
+def test_predict_proba(fitted_logistic_regression):
+    proba = fitted_logistic_regression.predict_proba([[7, 8], [5, 6], [3, 4], [1, 2]])
+    assert len(proba) == 2
+    assert proba[0].shape == (4, 2)
+    assert proba[1].shape == (4, 4)
+    assert_array_almost_equal(
+        np.sum(proba[0], axis=1), np.ones(len(proba[0])), decimal=10
+    )
+    assert_array_almost_equal(
+        np.sum(proba[1], axis=1), np.ones(len(proba[1])), decimal=10
+    )
+
+
+def test_predict_proba_sparse(fitted_logistic_regression):
+    proba = fitted_logistic_regression.predict_proba(
+        csr_matrix([[7, 8], [5, 6], [3, 4], [1, 2]])
+    )
+    assert len(proba) == 2
+    assert proba[0].shape == (4, 2)
+    assert proba[1].shape == (4, 4)
+    assert_array_almost_equal(
+        np.sum(proba[0], axis=1), np.ones(len(proba[0])), decimal=10
+    )
+    assert_array_almost_equal(
+        np.sum(proba[1], axis=1), np.ones(len(proba[1])), decimal=10
+    )
+
+
 def test_fit_predict():
     lcpn = LocalClassifierPerNode(local_classifier=LogisticRegression())
     x = np.array([[1, 2], [3, 4]])
@@ -196,3 +385,47 @@ def test_fit_predict():
     lcpn.fit(x, y)
     predictions = lcpn.predict(x)
     assert_array_equal(y, predictions)
+
+
+def test_fit_calibrate_predict_proba():
+    lcpn = LocalClassifierPerNode(
+        local_classifier=LogisticRegression(),
+        calibration_method="ivap",
+        return_all_probabilities=True,
+    )
+    x = np.array([[1, 2], [3, 4]])
+    y = np.array([["a", "b"], ["b", "c"]])
+    lcpn.fit(x, y)
+    lcpn.calibrate(x, y)
+    predictions = lcpn.predict(x)
+    proba = lcpn.predict_proba(x)
+    assert_array_equal(y, predictions)
+
+    assert len(proba) == 2
+    assert proba[0].shape == (2, 2)
+    assert proba[1].shape == (2, 2)
+    assert_array_almost_equal(
+        np.sum(proba[0], axis=1), np.ones(len(proba[0])), decimal=10
+    )
+    assert_array_almost_equal(
+        np.sum(proba[1], axis=1), np.ones(len(proba[1])), decimal=10
+    )
+
+
+def test_fit_calibrate_predict_predict_proba_bert():
+    classifier = LocalClassifierPerNode(
+        local_classifier=LogisticRegression(),
+        return_all_probabilities=True,
+        calibration_method="ivap",
+        probability_combiner="geometric",
+    )
+
+    classifier.logger_ = logging.getLogger("HC")
+    classifier.bert = True
+    x = [[0, 1], [2, 3]]
+    y = [["a", "b"], ["c", "d"]]
+    sample_weight = None
+    classifier.fit(x, y, sample_weight)
+    classifier.calibrate(x, y)
+    classifier.predict(x)
+    classifier.predict_proba(x)
diff --git a/tests/test_LocalClassifierPerParentNode.py b/tests/test_LocalClassifierPerParentNode.py
index 9d01d605..ad1a5469 100644
--- a/tests/test_LocalClassifierPerParentNode.py
+++ b/tests/test_LocalClassifierPerParentNode.py
@@ -4,13 +4,15 @@
 import networkx as nx
 import numpy as np
 import pytest
-from numpy.testing import assert_array_equal
+from numpy.testing import assert_array_equal, assert_array_almost_equal
 from scipy.sparse import csr_matrix
 from sklearn.exceptions import NotFittedError
 from sklearn.linear_model import LogisticRegression
 from sklearn.utils.estimator_checks import parametrize_with_checks
 from sklearn.utils.validation import check_is_fitted
 from hiclass import LocalClassifierPerParentNode
+from hiclass._calibration.Calibrator import _Calibrator
+from hiclass.HierarchicalClassifier import make_leveled
 
 
 @parametrize_with_checks([LocalClassifierPerParentNode()])
@@ -20,10 +22,14 @@ def test_sklearn_compatible_estimator(estimator, check):
 
 @pytest.fixture
 def digraph_logistic_regression():
-    digraph = LocalClassifierPerParentNode(local_classifier=LogisticRegression())
-    digraph.hierarchy_ = nx.DiGraph([("a", "b"), ("a", "c")])
-    digraph.y_ = np.array([["a", "b"], ["a", "c"]])
-    digraph.X_ = np.array([[1, 2], [3, 4]])
+    digraph = LocalClassifierPerParentNode(
+        local_classifier=LogisticRegression(), calibration_method="ivap"
+    )
+    digraph.hierarchy_ = nx.DiGraph([("a", "b"), ("a", "c"), ("b", "d"), ("c", "f")])
+    digraph.y_ = np.array([["a", "b"], ["a", "c"], ["b", "d"], ["c", "f"]])
+    digraph.X_ = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
+    digraph.y_cal = np.array([["a", "b"], ["a", "c"], ["b", "d"], ["c", "f"]])
+    digraph.X_cal = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
     digraph.logger_ = logging.getLogger("LCPPN")
     digraph.root_ = "a"
     digraph.separator_ = "::HiClass::Separator::"
@@ -34,7 +40,7 @@ def digraph_logistic_regression():
 def test_initialize_local_classifiers(digraph_logistic_regression):
     digraph_logistic_regression._initialize_local_classifiers()
     for node in digraph_logistic_regression.hierarchy_.nodes:
-        if node == digraph_logistic_regression.root_:
+        if node in ["a", "b", "c"]:
             assert isinstance(
                 digraph_logistic_regression.hierarchy_.nodes[node]["classifier"],
                 LogisticRegression,
@@ -47,9 +53,29 @@ def test_initialize_local_classifiers(digraph_logistic_regression):
                 )
 
 
+def test_initialize_local_calibrators(digraph_logistic_regression):
+    digraph_logistic_regression._initialize_local_classifiers()
+    digraph_logistic_regression._initialize_local_calibrators()
+
+    for node in digraph_logistic_regression.hierarchy_.nodes:
+        if node in ["a", "b", "c"]:
+            assert isinstance(
+                digraph_logistic_regression.hierarchy_.nodes[node]["calibrator"],
+                _Calibrator,
+            )
+        else:
+            with pytest.raises(KeyError):
+                isinstance(
+                    digraph_logistic_regression.hierarchy_.nodes[node]["classifier"],
+                    _Calibrator,
+                )
+
+
 def test_fit_digraph(digraph_logistic_regression):
     classifiers = {
         "a": {"classifier": LogisticRegression()},
+        "b": {"classifier": LogisticRegression()},
+        "c": {"classifier": LogisticRegression()},
     }
     digraph_logistic_regression.n_jobs = 2
     nx.set_node_attributes(digraph_logistic_regression.hierarchy_, classifiers)
@@ -58,7 +84,7 @@ def test_fit_digraph(digraph_logistic_regression):
         check_is_fitted(digraph_logistic_regression.hierarchy_.nodes["a"]["classifier"])
     except NotFittedError as e:
         pytest.fail(repr(e))
-    for node in ["b", "c"]:
+    for node in ["d", "f"]:
         with pytest.raises(KeyError):
             check_is_fitted(
                 digraph_logistic_regression.hierarchy_.nodes[node]["classifier"]
@@ -66,9 +92,43 @@ def test_fit_digraph(digraph_logistic_regression):
     assert 1
 
 
+def test_calibrate_digraph(digraph_logistic_regression):
+    classifiers = {
+        "a": {"classifier": LogisticRegression()},
+        "b": {"classifier": LogisticRegression()},
+        "c": {"classifier": LogisticRegression()},
+    }
+    digraph_logistic_regression.n_jobs = 2
+    nx.set_node_attributes(digraph_logistic_regression.hierarchy_, classifiers)
+    digraph_logistic_regression._fit_digraph(local_mode=True)
+
+    calibrators = {
+        "a": {
+            "calibrator": _Calibrator(
+                digraph_logistic_regression.hierarchy_.nodes["a"]["classifier"]
+            )
+        },
+    }
+    nx.set_node_attributes(digraph_logistic_regression.hierarchy_, calibrators)
+    digraph_logistic_regression._calibrate_digraph(local_mode=True)
+
+    try:
+        check_is_fitted(digraph_logistic_regression.hierarchy_.nodes["a"]["calibrator"])
+    except NotFittedError as e:
+        pytest.fail(repr(e))
+    for node in ["b", "c", "d", "f"]:
+        with pytest.raises((TypeError, KeyError)):
+            check_is_fitted(
+                digraph_logistic_regression.hierarchy_.nodes[node]["calibrator"]
+            )
+    assert 1
+
+
 def test_fit_digraph_joblib_multiprocessing(digraph_logistic_regression):
     classifiers = {
         "a": {"classifier": LogisticRegression()},
+        "b": {"classifier": LogisticRegression()},
+        "c": {"classifier": LogisticRegression()},
     }
     digraph_logistic_regression.n_jobs = 2
     nx.set_node_attributes(digraph_logistic_regression.hierarchy_, classifiers)
@@ -77,7 +137,7 @@ def test_fit_digraph_joblib_multiprocessing(digraph_logistic_regression):
         check_is_fitted(digraph_logistic_regression.hierarchy_.nodes["a"]["classifier"])
     except NotFittedError as e:
         pytest.fail(repr(e))
-    for node in ["b", "c"]:
+    for node in ["d", "f"]:
         with pytest.raises(KeyError):
             check_is_fitted(
                 digraph_logistic_regression.hierarchy_.nodes[node]["classifier"]
@@ -85,6 +145,38 @@ def test_fit_digraph_joblib_multiprocessing(digraph_logistic_regression):
     assert 1
 
 
+def test_calibrate_digraph_joblib_multiprocessing(digraph_logistic_regression):
+    classifiers = {
+        "a": {"classifier": LogisticRegression()},
+        "b": {"classifier": LogisticRegression()},
+        "c": {"classifier": LogisticRegression()},
+    }
+    digraph_logistic_regression.n_jobs = 2
+    nx.set_node_attributes(digraph_logistic_regression.hierarchy_, classifiers)
+    digraph_logistic_regression._fit_digraph(local_mode=True, use_joblib=True)
+
+    calibrators = {
+        "a": {
+            "calibrator": _Calibrator(
+                digraph_logistic_regression.hierarchy_.nodes["a"]["classifier"]
+            )
+        },
+    }
+    nx.set_node_attributes(digraph_logistic_regression.hierarchy_, calibrators)
+    digraph_logistic_regression._calibrate_digraph(local_mode=True, use_joblib=True)
+
+    try:
+        check_is_fitted(digraph_logistic_regression.hierarchy_.nodes["a"]["calibrator"])
+    except NotFittedError as e:
+        pytest.fail(repr(e))
+    for node in ["b", "c", "d", "f"]:
+        with pytest.raises((TypeError, KeyError)):
+            check_is_fitted(
+                digraph_logistic_regression.hierarchy_.nodes[node]["calibrator"]
+            )
+    assert 1
+
+
 @pytest.fixture
 def digraph_2d():
     classifier = LocalClassifierPerParentNode()
@@ -132,32 +224,79 @@ def test_get_successors(x_and_y_arrays):
 
 @pytest.fixture
 def fitted_logistic_regression():
-    digraph = LocalClassifierPerParentNode(local_classifier=LogisticRegression())
+    digraph = LocalClassifierPerParentNode(
+        local_classifier=LogisticRegression(),
+        calibration_method="ivap",
+        probability_combiner="geometric",
+        return_all_probabilities=True,
+    )
+
+    digraph.separator_ = "::HiClass::Separator::"
     digraph.hierarchy_ = nx.DiGraph(
         [
             ("r", "1"),
             ("r", "2"),
-            ("1", "1.1"),
-            ("1", "1.2"),
-            ("2", "2.1"),
-            ("2", "2.2"),
+            ("1", "1" + digraph.separator_ + "1.1"),
+            ("1", "1" + digraph.separator_ + "1.2"),
+            ("2", "2" + digraph.separator_ + "2.1"),
+            ("2", "2" + digraph.separator_ + "2.2"),
         ]
     )
     digraph.y_ = np.array([["1", "1.1"], ["1", "1.2"], ["2", "2.1"], ["2", "2.2"]])
     digraph.X_ = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
     digraph.logger_ = logging.getLogger("LCPPN")
     digraph.max_levels_ = 2
-    digraph.dtype_ = "<U3"
+    digraph.max_level_dimensions_ = np.array([2, 4])
+    digraph.dtype_ = "<U30"
     digraph.root_ = "r"
-    digraph.separator_ = "::HiClass::Separator::"
+
+    # for predict_proba
+    tmp_labels = digraph._disambiguate(make_leveled(digraph.y_))
+    # digraph.y_ = tmp_labels
+    digraph.global_classes_ = [
+        np.unique(tmp_labels[:, level]).astype("str")
+        for level in range(tmp_labels.shape[1])
+    ]
+    digraph.global_class_to_index_mapping_ = [
+        {
+            digraph.global_classes_[level][index]: index
+            for index in range(len(digraph.global_classes_[level]))
+        }
+        for level in range(tmp_labels.shape[1])
+    ]
+
+    classes_ = [digraph.global_classes_[0]]
+    for level in range(1, digraph.max_levels_):
+        classes_.append(
+            np.sort(
+                np.unique(
+                    [
+                        label.split(digraph.separator_)[level]
+                        for label in digraph.global_classes_[level]
+                    ]
+                )
+            )
+        )
+    digraph.classes_ = classes_
+    digraph.class_to_index_mapping_ = [
+        {local_labels[index]: index for index in range(len(local_labels))}
+        for local_labels in classes_
+    ]
+
     classifiers = {
         "r": {"classifier": LogisticRegression()},
         "1": {"classifier": LogisticRegression()},
         "2": {"classifier": LogisticRegression()},
     }
     classifiers["r"]["classifier"].fit(digraph.X_, ["1", "1", "2", "2"])
-    classifiers["1"]["classifier"].fit(digraph.X_[:2], ["1.1", "1.2"])
-    classifiers["2"]["classifier"].fit(digraph.X_[2:], ["2.1", "2.2"])
+    classifiers["1"]["classifier"].fit(
+        digraph.X_[:2],
+        ["1" + digraph.separator_ + "1.1", "1" + digraph.separator_ + "1.2"],
+    )
+    classifiers["2"]["classifier"].fit(
+        digraph.X_[2:],
+        ["2" + digraph.separator_ + "2.1", "2" + digraph.separator_ + "2.2"],
+    )
     nx.set_node_attributes(digraph.hierarchy_, classifiers)
     return digraph
 
@@ -176,6 +315,34 @@ def test_predict_sparse(fitted_logistic_regression):
     assert_array_equal(ground_truth, prediction)
 
 
+def test_predict_proba(fitted_logistic_regression):
+    proba = fitted_logistic_regression.predict_proba([[7, 8], [5, 6], [3, 4], [1, 2]])
+    assert len(proba) == 2
+    assert proba[0].shape == (4, 2)
+    assert proba[1].shape == (4, 4)
+    assert_array_almost_equal(
+        np.sum(proba[0], axis=1), np.ones(len(proba[0])), decimal=10
+    )
+    assert_array_almost_equal(
+        np.sum(proba[1], axis=1), np.ones(len(proba[1])), decimal=10
+    )
+
+
+def test_predict_proba_sparse(fitted_logistic_regression):
+    proba = fitted_logistic_regression.predict_proba(
+        csr_matrix([[7, 8], [5, 6], [3, 4], [1, 2]])
+    )
+    assert len(proba) == 2
+    assert proba[0].shape == (4, 2)
+    assert proba[1].shape == (4, 4)
+    assert_array_almost_equal(
+        np.sum(proba[0], axis=1), np.ones(len(proba[0])), decimal=10
+    )
+    assert_array_almost_equal(
+        np.sum(proba[1], axis=1), np.ones(len(proba[1])), decimal=10
+    )
+
+
 def test_fit_predict():
     lcppn = LocalClassifierPerParentNode(local_classifier=LogisticRegression())
     x = np.array([[1, 2], [3, 4]])
@@ -183,3 +350,46 @@ def test_fit_predict():
     lcppn.fit(x, y)
     predictions = lcppn.predict(x)
     assert_array_equal(y, predictions)
+
+
+def test_fit_calibrate_predict_proba():
+    lcppn = LocalClassifierPerParentNode(
+        local_classifier=LogisticRegression(),
+        calibration_method="ivap",
+        return_all_probabilities=True,
+    )
+    x = np.array([[1, 2], [3, 4]])
+    y = np.array([["a", "b"], ["b", "c"]])
+    lcppn.fit(x, y)
+    predictions = lcppn.predict(x)
+    proba = lcppn.predict_proba(x)
+    assert_array_equal(y, predictions)
+
+    assert len(proba) == 2
+    assert proba[0].shape == (2, 2)
+    assert proba[1].shape == (2, 2)
+    assert_array_almost_equal(
+        np.sum(proba[0], axis=1), np.ones(len(proba[0])), decimal=10
+    )
+    assert_array_almost_equal(
+        np.sum(proba[1], axis=1), np.ones(len(proba[1])), decimal=10
+    )
+
+
+def test_fit_calibrate_predict_predict_proba_bert():
+    classifier = LocalClassifierPerParentNode(
+        local_classifier=LogisticRegression(),
+        return_all_probabilities=True,
+        calibration_method="ivap",
+        probability_combiner="geometric",
+    )
+
+    classifier.logger_ = logging.getLogger("HC")
+    classifier.bert = True
+    x = [[0, 1], [2, 3]]
+    y = [["a", "b"], ["c", "d"]]
+    sample_weight = None
+    classifier.fit(x, y, sample_weight)
+    classifier.calibrate(x, y)
+    classifier.predict(x)
+    classifier.predict_proba(x)
diff --git a/tests/test_ProbabilityCombiner.py b/tests/test_ProbabilityCombiner.py
new file mode 100644
index 00000000..a9f21aa7
--- /dev/null
+++ b/tests/test_ProbabilityCombiner.py
@@ -0,0 +1,316 @@
+import numpy as np
+import pytest
+from unittest.mock import Mock
+import networkx as nx
+from numpy.testing import assert_array_almost_equal, assert_array_equal
+import math
+from scipy.stats import gmean
+
+from hiclass.HierarchicalClassifier import make_leveled
+
+from hiclass.HierarchicalClassifier import HierarchicalClassifier
+from hiclass.probability_combiner import (
+    MultiplyCombiner,
+    ArithmeticMeanCombiner,
+    GeometricMeanCombiner,
+)
+
+
+def _disambiguate(y, separator):
+    if y.ndim == 2:
+        new_y = []
+        for i in range(y.shape[0]):
+            row = [str(y[i, 0])]
+            for j in range(1, y.shape[1]):
+                parent = str(row[-1])
+                child = str(y[i, j])
+                row.append(parent + separator + child)
+            new_y.append(np.asarray(row, dtype=np.str_))
+        return np.array(new_y)
+    return y
+
+
+@pytest.fixture
+def one_sample_probs_with_hierarchy():
+    hierarchy = nx.DiGraph()
+    root_node = "hiclass::root"
+    hierarchy.add_node(root_node)
+    separator = "::HiClass::Separator::"
+    y_ = []
+
+    for i in range(3):
+        # first level
+        first_level_node = f"node_{i}"
+        hierarchy.add_node(first_level_node)
+        hierarchy.add_edge(root_node, first_level_node)
+        for j in range(3):
+            # second level
+            second_level_node = f"node_{i}:{j}"
+            second_level_label = _disambiguate(
+                np.array([[first_level_node, second_level_node]]), separator
+            )[0][1]
+            hierarchy.add_node(second_level_label)
+            hierarchy.add_edge(first_level_node, second_level_label)
+            for k in range(2):
+                # third level
+                third_level_node = f"node_{i}:{j}:{k}"
+                third_level_label = _disambiguate(
+                    np.array([[first_level_node, second_level_node, third_level_node]]),
+                    separator,
+                )[0][2]
+                y_.append([first_level_node, second_level_node, third_level_node])
+                hierarchy.add_node(third_level_label)
+                hierarchy.add_edge(second_level_label, third_level_label)
+
+    probs = [
+        np.array([[0.3, 0.5, 0.2]]),  # level 0
+        np.array([[0.19, 0.10, 0.16, 0.14, 0.07, 0.08, 0.19, 0.03, 0.04]]),  # level 1
+        np.array(
+            [
+                [
+                    0.03,
+                    0.17,
+                    0.07,
+                    0.01,
+                    0.00,
+                    0.06,
+                    0.17,
+                    0.02,
+                    0.01,
+                    0.07,
+                    0.20,
+                    0.01,
+                    0.10,
+                    0.00,
+                    0.05,
+                    0.02,
+                    0.00,
+                    0.01,
+                ]
+            ]
+        ),  # level 2
+    ]
+    assert all([np.sum(probs[level], axis=1) == 1 for level in range(3)])
+
+    y_ = np.array(y_)
+    y_ = make_leveled(y_)
+    y_ = _disambiguate(y_, separator)
+
+    global_classes_ = [
+        np.unique(y_[:, level]).astype("str") for level in range(y_.shape[1])
+    ]
+
+    classes_ = [global_classes_[0]]
+    for level in range(1, len(y_[1])):
+        classes_.append(
+            np.sort(
+                np.unique(
+                    [label.split(separator)[level] for label in global_classes_[level]]
+                )
+            )
+        )
+
+    class_to_index_mapping_ = [
+        {local_labels[index]: index for index in range(len(local_labels))}
+        for local_labels in classes_
+    ]
+
+    return hierarchy, probs, global_classes_, classes_, class_to_index_mapping_
+
+
+def test_multiply_combiner(one_sample_probs_with_hierarchy):
+    (
+        hierarchy,
+        probs,
+        global_classes,
+        classes_,
+        class_to_index_mapping_,
+    ) = one_sample_probs_with_hierarchy
+    obj = HierarchicalClassifier()
+    classifier = Mock(spec=obj)
+    classifier._disambiguate = obj._disambiguate
+    classifier.global_classes_ = global_classes
+    classifier.max_levels_ = len(global_classes)
+    classifier.classes_ = classes_
+    classifier.class_to_index_mapping_ = class_to_index_mapping_
+
+    classifier.hierarchy_ = hierarchy
+    classifier.separator_ = "::HiClass::Separator::"
+
+    combiner = MultiplyCombiner(classifier=classifier, normalize=False)
+    combined_probs = combiner.combine(probs)
+
+    # check combined probability of first node for both levels
+    assert math.isclose(combined_probs[1][0][0], 0.0569, abs_tol=1e-4)
+    assert math.isclose(
+        combined_probs[1][0][0], probs[0][0][0] * probs[1][0][0], abs_tol=1e-4
+    )
+
+    assert math.isclose(combined_probs[2][0][0], 0.0017, abs_tol=1e-4)
+    assert math.isclose(
+        combined_probs[2][0][0],
+        probs[0][0][0] * probs[1][0][0] * probs[2][0][0],
+        abs_tol=1e-4,
+    )
+
+    # check combined probability of last node for both levels
+    assert math.isclose(combined_probs[1][0][-1], 0.008, abs_tol=1e-4)
+    assert math.isclose(
+        combined_probs[1][0][-1], probs[0][0][-1] * probs[1][0][-1], abs_tol=1e-4
+    )
+
+    assert math.isclose(combined_probs[2][0][-1], 8e-5, abs_tol=1e-4)
+    assert math.isclose(
+        combined_probs[2][0][-1],
+        probs[0][0][-1] * probs[1][0][-1] * probs[2][0][-1],
+        abs_tol=1e-4,
+    )
+
+
+def test_arithmetic_mean_combiner(one_sample_probs_with_hierarchy):
+    (
+        hierarchy,
+        probs,
+        global_classes,
+        classes_,
+        class_to_index_mapping_,
+    ) = one_sample_probs_with_hierarchy
+    obj = HierarchicalClassifier()
+    classifier = Mock(spec=obj)
+    classifier._disambiguate = obj._disambiguate
+
+    classifier.global_classes_ = global_classes
+    classifier.max_levels_ = len(global_classes)
+    classifier.classes_ = classes_
+    classifier.class_to_index_mapping_ = class_to_index_mapping_
+    classifier.hierarchy_ = hierarchy
+    classifier.separator_ = "::HiClass::Separator::"
+
+    combiner = ArithmeticMeanCombiner(classifier=classifier, normalize=False)
+    combined_probs = combiner.combine(probs)
+
+    # check combined probability of first node for both levels
+    assert math.isclose(combined_probs[1][0][0], 0.245, abs_tol=1e-4)
+    assert math.isclose(
+        combined_probs[1][0][0], (probs[0][0][0] + probs[1][0][0]) / 2, abs_tol=1e-4
+    )
+
+    assert math.isclose(combined_probs[2][0][0], 0.1733, abs_tol=1e-4)
+    assert math.isclose(
+        combined_probs[2][0][0],
+        (probs[0][0][0] + probs[1][0][0] + probs[2][0][0]) / 3,
+        abs_tol=1e-4,
+    )
+
+    # check combined probability of last node for both levels
+    assert math.isclose(combined_probs[1][0][-1], 0.12, abs_tol=1e-4)
+    assert math.isclose(
+        combined_probs[1][0][-1], (probs[0][0][-1] + probs[1][0][-1]) / 2, abs_tol=1e-4
+    )
+
+    assert math.isclose(combined_probs[2][0][-1], 0.0833, abs_tol=1e-4)
+    assert math.isclose(
+        combined_probs[2][0][-1],
+        (probs[0][0][-1] + probs[1][0][-1] + probs[2][0][-1]) / 3,
+        abs_tol=1e-4,
+    )
+
+
+def test_geometric_mean_combiner(one_sample_probs_with_hierarchy):
+    (
+        hierarchy,
+        probs,
+        global_classes,
+        classes_,
+        class_to_index_mapping_,
+    ) = one_sample_probs_with_hierarchy
+    obj = HierarchicalClassifier()
+    classifier = Mock(spec=obj)
+    classifier._disambiguate = obj._disambiguate
+    classifier.separator_ = "::HiClass::Separator::"
+    classifier.global_classes_ = global_classes
+    classifier.classes_ = classes_
+
+    classifier.max_levels_ = len(global_classes)
+    classifier.class_to_index_mapping_ = class_to_index_mapping_
+    classifier.hierarchy_ = hierarchy
+
+    combiner = GeometricMeanCombiner(classifier=classifier, normalize=False)
+    combined_probs = combiner.combine(probs)
+
+    # check combined probability of first node for both levels
+    assert math.isclose(combined_probs[1][0][0], 0.2387, abs_tol=1e-4)
+    assert math.isclose(
+        combined_probs[1][0][0], gmean([probs[0][0][0], probs[1][0][0]]), abs_tol=1e-4
+    )
+
+    assert math.isclose(combined_probs[2][0][0], 0.1195, abs_tol=1e-4)
+    assert math.isclose(
+        combined_probs[2][0][0],
+        gmean([probs[0][0][0], probs[1][0][0], probs[2][0][0]]),
+        abs_tol=1e-4,
+    )
+
+    # check combined probability of last node for both levels
+    assert math.isclose(combined_probs[1][0][-1], 0.0894, abs_tol=1e-4)
+    assert math.isclose(
+        combined_probs[1][0][-1],
+        gmean([probs[0][0][-1], probs[1][0][-1]]),
+        abs_tol=1e-4,
+    )
+
+    assert math.isclose(combined_probs[2][0][-1], 0.0430, abs_tol=1e-4)
+    assert math.isclose(
+        combined_probs[2][0][-1],
+        gmean([probs[0][0][-1], probs[1][0][-1], probs[2][0][-1]]),
+        abs_tol=1e-4,
+    )
+
+
+def test_mean_combiner_with_normalization(one_sample_probs_with_hierarchy):
+    (
+        hierarchy,
+        probs,
+        global_classes,
+        classes_,
+        class_to_index_mapping_,
+    ) = one_sample_probs_with_hierarchy
+    obj = HierarchicalClassifier()
+    classifier = Mock(spec=obj)
+    classifier._disambiguate = obj._disambiguate
+    classifier.separator_ = "::HiClass::Separator::"
+    classifier.global_classes_ = global_classes
+    classifier.classes_ = classes_
+
+    classifier.max_levels_ = len(global_classes)
+    classifier.class_to_index_mapping_ = class_to_index_mapping_
+    classifier.hierarchy_ = hierarchy
+
+    combiners = [
+        MultiplyCombiner(classifier),
+        ArithmeticMeanCombiner(classifier),
+        GeometricMeanCombiner(classifier),
+    ]
+
+    for combiner in combiners:
+        combined_probs = combiner.combine(probs)
+
+        assert len(combined_probs) == 3
+        assert combined_probs[0].shape == (1, 3)
+        assert combined_probs[1].shape == (1, 9)
+        assert combined_probs[2].shape == (1, 18)
+        assert_array_almost_equal(
+            np.sum(combined_probs[0], axis=1),
+            np.ones(len(combined_probs[0])),
+            decimal=10,
+        )
+        assert_array_almost_equal(
+            np.sum(combined_probs[1], axis=1),
+            np.ones(len(combined_probs[1])),
+            decimal=10,
+        )
+        assert_array_almost_equal(
+            np.sum(combined_probs[2], axis=1),
+            np.ones(len(combined_probs[2])),
+            decimal=10,
+        )
diff --git a/tests/test_calibration.py b/tests/test_calibration.py
new file mode 100644
index 00000000..6e419895
--- /dev/null
+++ b/tests/test_calibration.py
@@ -0,0 +1,422 @@
+import pytest
+import numpy as np
+from unittest.mock import Mock
+from numpy.testing import assert_array_almost_equal, assert_array_equal
+from sklearn.linear_model import LogisticRegression
+from sklearn.exceptions import NotFittedError
+
+from hiclass._calibration.VennAbersCalibrator import (
+    _InductiveVennAbersCalibrator,
+    _CrossVennAbersCalibrator,
+)
+from hiclass._calibration.PlattScaling import _PlattScaling
+from hiclass._calibration.IsotonicRegression import _IsotonicRegression
+from hiclass._calibration.BetaCalibrator import _BetaCalibrator
+from hiclass._calibration.Calibrator import _Calibrator
+
+
+@pytest.fixture
+def binary_calibration_data():
+    prob = np.array(
+        [
+            [0.37, 0.63],
+            [0.39, 0.61],
+            [0.42, 0.58],
+            [0.51, 0.49],
+            [0.51, 0.49],
+            [0.45, 0.55],
+            [0.48, 0.52],
+            [0.60, 0.40],
+            [0.54, 0.46],
+            [0.57, 0.43],
+            [0.57, 0.43],
+            [0.62, 0.38],
+        ]
+    )
+
+    assert_array_equal(np.sum(prob, axis=1), np.ones(len(prob)))
+
+    ground_truth_labels = np.array([1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0])
+    return prob, ground_truth_labels
+
+
+@pytest.fixture
+def binary_test_scores():
+    X = np.array(
+        [
+            [0.22, 0.78],
+            [0.48, 0.52],
+            [0.66, 0.34],
+            [0.01, 0.99],
+            [0.77, 0.23],
+            [0.50, 0.50],
+            [1.00, 0.00],
+        ]
+    )
+
+    assert_array_equal(np.sum(X, axis=1), np.ones(len(X)))
+    return X
+
+
+@pytest.fixture
+def binary_cal_X():
+    X = np.array(
+        [
+            [1, 2],
+            [3, 4],
+            [5, 6],
+            [7, 8],
+            [1, 2],
+            [3, 4],
+            [5, 6],
+            [7, 8],
+            [1, 2],
+            [3, 4],
+            [5, 6],
+            [7, 8],
+        ]
+    )
+    return X
+
+
+@pytest.fixture
+def binary_mock_estimator(binary_calibration_data, binary_test_scores):
+    # return calibration scores or test scores depending on input size
+    def side_effect(X):
+        return (
+            binary_calibration_data[0]
+            if len(X) == len(binary_calibration_data[0])
+            else binary_test_scores
+        )
+
+    lr = LogisticRegression()
+    binary_estimator = Mock(spec=lr)
+    binary_estimator.predict_proba.side_effect = side_effect
+    binary_estimator.classes_ = np.array([0, 1])
+    binary_estimator.get_params.return_value = lr.get_params()
+    return binary_estimator
+
+
+@pytest.fixture
+def multiclass_calibration_data():
+    prob = np.array(
+        [
+            [0.12, 0.24, 0.64],
+            [0.44, 0.22, 0.34],
+            [0.30, 0.40, 0.30],
+            [0.11, 0.33, 0.56],
+            [0.44, 0.22, 0.34],
+            [0.66, 0.33, 0.01],
+            [0.14, 0.66, 0.20],
+            [0.44, 0.12, 0.44],
+            [0.64, 0.24, 0.12],
+            [0.20, 0.77, 0.03],
+        ]
+    )
+
+    assert_array_equal(np.sum(prob, axis=1), np.ones(len(prob)))
+
+    ground_truth_labels = np.array([2, 0, 1, 2, 0, 0, 1, 0, 0, 1])
+    return prob, ground_truth_labels
+
+
+@pytest.fixture
+def multiclass_test_scores():
+    X = np.array(
+        [
+            [0.23, 0.47, 0.30],
+            [0.44, 0.21, 0.35],
+            [0.22, 0.22, 0.56],
+            [0.25, 0.50, 0.25],
+            [0.01, 0.72, 0.27],
+            [0.12, 0.35, 0.53],
+            [0.30, 0.23, 0.47],
+        ]
+    )
+
+    assert_array_equal(np.sum(X, axis=1), np.ones(len(X)))
+    return X
+
+
+@pytest.fixture
+def multiclass_mock_estimator(multiclass_calibration_data, multiclass_test_scores):
+    # return calibration scores or test scores depending on input size
+    def side_effect(X):
+        return (
+            multiclass_calibration_data[0]
+            if len(X) == len(multiclass_calibration_data[0])
+            else multiclass_test_scores
+        )
+
+    multiclass_estimator = Mock(spec=LogisticRegression)
+    multiclass_estimator.predict_proba.side_effect = side_effect
+    multiclass_estimator.classes_ = np.array([0, 1, 2])
+    return multiclass_estimator
+
+
+def test_inductive_venn_abers_calibrator(binary_calibration_data, binary_test_scores):
+    scores, ground_truth_labels = binary_calibration_data
+    test_scores = binary_test_scores
+    calibrator = _InductiveVennAbersCalibrator()
+    calibrator.fit(scores=scores[:, 1], y=ground_truth_labels)
+
+    intervalls = calibrator.predict_intervall(test_scores[:, 1])
+    proba = calibrator.predict_proba(test_scores[:, 1])
+
+    assert_array_almost_equal(
+        calibrator._F1,
+        np.array(
+            [
+                0,
+                0.33333333,
+                0.375,
+                0.375,
+                0.4,
+                0.5,
+                0.5,
+                0.66666666,
+                0.66666666,
+                1.0,
+                1.0,
+            ]
+        ),
+    )
+    assert_array_almost_equal(
+        calibrator._F0,
+        np.array(
+            [0, 0, 0.2, 0.2, 0.2, 0.25, 0.25, 0.33333333, 0.33333333, 0.5, 0.66666666]
+        ),
+    )
+    assert_array_almost_equal(
+        intervalls,
+        np.array(
+            [
+                [0.66666666, 1.0],
+                [0.25, 0.66666666],
+                [0, 0.33333333],
+                [0.66666666, 1.0],
+                [0, 0.33333333],
+                [0.25, 0.5],
+                [0, 0.33333333],
+            ]
+        ),
+    )
+    assert_array_almost_equal(
+        proba,
+        np.array(
+            [
+                0.74999999,
+                0.47058823,
+                0.24999999,
+                0.74999999,
+                0.24999999,
+                0.4,
+                0.24999999,
+            ]
+        ),
+    )
+
+
+def test_cross_venn_abers_calibrator(
+    binary_calibration_data, binary_test_scores, binary_cal_X
+):
+    cal_scores, y_cal = binary_calibration_data
+    test_scores = binary_test_scores
+
+    calibrator = _CrossVennAbersCalibrator(LogisticRegression(), n_folds=5)
+    calibrator.fit(y_cal, cal_scores, X=binary_cal_X)
+    proba = calibrator.predict_proba(test_scores[:, 1])
+    expected = np.array(
+        [0.602499, 0.51438, 0.397501, 0.602499, 0.346239, 0.51438, 0.328119]
+    )
+    assert len(calibrator.ivaps) == 5
+    assert_array_almost_equal(proba, expected)
+
+
+def test_platt_scaling(binary_calibration_data, binary_test_scores):
+    cal_scores, cal_labels = binary_calibration_data
+    calibrator = _PlattScaling()
+    calibrator.fit(cal_labels, cal_scores[:, 1])
+    proba = calibrator.predict_proba(binary_test_scores[:, 1])
+
+    assert proba.shape == (len(binary_test_scores),)
+    assert_array_almost_equal(
+        proba,
+        np.array(
+            [
+                0.8827398,
+                0.46130191,
+                0.15976229,
+                0.97756289,
+                0.07045925,
+                0.42011212,
+                0.01095897,
+            ]
+        ),
+    )
+
+
+def test_isotonic_regression(binary_calibration_data, binary_test_scores):
+    cal_scores, cal_labels = binary_calibration_data
+    calibrator = _IsotonicRegression()
+    calibrator.fit(cal_labels, cal_scores[:, 1])
+    proba = calibrator.predict_proba(binary_test_scores[:, 1])
+
+    assert proba.shape == (len(binary_test_scores),)
+    assert_array_almost_equal(
+        proba, np.array([1.0, 0.33333333, 0.0, 1.0, 0.0, 0.33333333, 0.0])
+    )
+
+
+def test_beta_calibration(binary_calibration_data, binary_test_scores):
+    cal_scores, cal_labels = binary_calibration_data
+    calibrator = _BetaCalibrator()
+    calibrator.fit(cal_labels, cal_scores[:, 1])
+    proba = calibrator.predict_proba(binary_test_scores[:, 1])
+
+    assert proba.shape == (len(binary_test_scores),)
+    assert_array_almost_equal(
+        proba,
+        np.array(
+            [0.526125, 0.423743, 0.363907, 0.785855, 0.323201, 0.417089, 0.0],
+        ),
+        decimal=3,
+    )
+
+
+def test_calibration_methods_can_handle_zeros(binary_test_scores):
+    cal_scores = np.array(
+        [
+            [0, 1],
+            [0, 1],
+            [0, 1],
+            [1, 0],
+            [1, 0],
+            [0, 1],
+            [0, 1],
+            [1, 0],
+            [1, 0],
+            [1, 0],
+            [1, 0],
+            [1, 0],
+        ],
+        dtype=np.float32,
+    )
+
+    cal_labels = np.array([1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0])
+    assert_array_equal(np.sum(cal_scores, axis=1), np.ones(len(cal_scores)))
+
+    for calibrator in [
+        _PlattScaling(),
+        _IsotonicRegression(),
+        _BetaCalibrator(),
+        _InductiveVennAbersCalibrator(),
+        _CrossVennAbersCalibrator(LogisticRegression()),
+    ]:
+        try:
+            calibrator.fit(cal_labels, cal_scores[:, 1], cal_scores)
+            calibrator.predict_proba(binary_test_scores[:, 1])
+        except ValueError as e:
+            pytest.fail(repr(e))
+
+
+def test_illegal_calibration_method_raises_error(binary_mock_estimator):
+    with pytest.raises(ValueError, match="abc is not a valid calibration method."):
+        _Calibrator(binary_mock_estimator, method="abc")
+
+
+def test_not_fitted_calibrator_throws_error(binary_test_scores, binary_mock_estimator):
+    for calibrator in [
+        _PlattScaling(),
+        _IsotonicRegression(),
+        _BetaCalibrator(),
+        _InductiveVennAbersCalibrator(),
+        _CrossVennAbersCalibrator(binary_mock_estimator),
+    ]:
+        with pytest.raises(NotFittedError):
+            calibrator.predict_proba(binary_test_scores)
+
+
+def test_valid_calibration(
+    binary_calibration_data, binary_test_scores, binary_cal_X, binary_mock_estimator
+):
+    _, y_cal = binary_calibration_data
+
+    for method in _Calibrator.available_methods:
+        lr = LogisticRegression()
+        lr.fit(binary_cal_X, y_cal)
+
+        calibrator = _Calibrator(lr, method)
+        calibrator.fit(X=binary_cal_X, y=y_cal)
+        proba = calibrator.predict_proba(binary_test_scores)
+
+        assert proba.shape == (len(binary_test_scores), 2)
+
+
+def test_multiclass_calibration(
+    multiclass_calibration_data, multiclass_test_scores, multiclass_mock_estimator
+):
+    scores, y_cal = multiclass_calibration_data
+
+    calibrator = _Calibrator(multiclass_mock_estimator, method="ivap")
+    calibrator.fit(X=scores, y=y_cal)
+    assert len(calibrator.calibrators) == scores.shape[1]
+
+    proba = calibrator.predict_proba(multiclass_test_scores)
+    assert proba.shape == multiclass_test_scores.shape
+    assert_array_almost_equal(np.sum(proba, axis=1), np.ones(len(proba)))
+
+    expected = np.array(
+        [
+            [0.27777778, 0.55555556, 0.16666667],
+            [0.52173913, 0.13043478, 0.34782609],
+            [0.33333333, 0.16666667, 0.5],
+            [0.28571429, 0.57142857, 0.14285714],
+            [0.13483146, 0.70786517, 0.15730337],
+            [0.16666667, 0.41666667, 0.41666667],
+            [0.42857143, 0.14285714, 0.42857143],
+        ]
+    )
+
+    assert_array_almost_equal(proba, expected)
+
+
+def test_multiclass_probability_merge(
+    multiclass_mock_estimator, multiclass_calibration_data, multiclass_test_scores
+):
+    calibrator = _Calibrator(estimator=multiclass_mock_estimator, method="ivap")
+    X_cal, y_cal = multiclass_calibration_data
+    calibrator.fit(X_cal, y_cal)
+
+    calibrator_1 = Mock(spec=_PlattScaling)
+    calibrator_1.predict_proba.return_value = np.array(
+        [0.40, 0.60, 0.20, 0.80, 0.75, 0.25, 0.85]
+    )
+
+    calibrator_2 = Mock(spec=_PlattScaling)
+    calibrator_2.predict_proba.return_value = np.array(
+        [0.30, 0.70, 0.35, 0.65, 0.30, 0.70, 0.20]
+    )
+
+    calibrator_3 = Mock(spec=_PlattScaling)
+    calibrator_3.predict_proba.return_value = np.array(
+        [0.55, 0.45, 0.10, 0.90, 0.45, 0.55, 0.40]
+    )
+
+    calibrator.calibrators = [calibrator_1, calibrator_2, calibrator_3]
+    proba = calibrator.predict_proba(multiclass_test_scores)
+
+    expected = np.array(
+        [
+            [0.32, 0.24, 0.44],
+            [0.34, 0.40, 0.26],
+            [0.31, 0.54, 0.15],
+            [0.34, 0.28, 0.38],
+            [0.5, 0.20, 0.30],
+            [0.17, 0.46, 0.37],
+            [0.59, 0.14, 0.27],
+        ]
+    )
+
+    assert_array_equal(np.sum(expected, axis=1), np.ones(len(expected)))
+    assert_array_almost_equal(proba, expected, decimal=2)
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index dceaf440..5125df40 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -1,8 +1,26 @@
 import numpy as np
 import pytest
 from pytest import approx
-
-from hiclass.metrics import precision, recall, f1
+import math
+from numpy.testing import assert_array_almost_equal, assert_array_equal
+from unittest.mock import Mock
+
+from hiclass.HierarchicalClassifier import HierarchicalClassifier
+from hiclass.metrics import (
+    precision,
+    recall,
+    f1,
+    _multiclass_brier_score,
+    _log_loss,
+    _expected_calibration_error,
+    _static_calibration_error,
+    _adaptive_calibration_error,
+    multiclass_brier_score,
+    log_loss,
+    expected_calibration_error,
+    static_calibration_error,
+    adaptive_calibration_error,
+)
 
 
 # TODO: add tests for 3D dataframe (not sure if it's possible to have 3D dataframes)
@@ -334,3 +352,274 @@ def test_empty_levels_2d_list_2():
     y_pred = [["1"], ["2", "3"], ["4", "5", "6"]]
     assert 1 == f1(y_true, y_pred)
     assert 1 == f1(y_true, y_true)
+
+
+@pytest.fixture
+def uncertainty_data():
+    prob = [
+        np.array(
+            [
+                [0.88, 0.06, 0.06],
+                [0.22, 0.48, 0.30],
+                [0.33, 0.33, 0.34],
+                [0.49, 0.40, 0.11],
+                [0.23, 0.03, 0.74],
+                [0.21, 0.67, 0.12],
+                [0.34, 0.34, 0.32],
+                [0.02, 0.77, 0.21],
+                [0.44, 0.42, 0.14],
+                [0.85, 0.13, 0.02],
+            ]
+        )
+    ]
+
+    assert_array_equal(np.sum(prob[0], axis=1), np.ones(len(prob[0])))
+
+    y_pred = np.array([[0], [1], [2], [0], [2], [1], [0], [1], [0], [0]])
+    y_true = np.array([[0], [2], [0], [0], [2], [1], [1], [1], [0], [0]])
+
+    return prob, y_pred, y_true
+
+
+@pytest.fixture
+def uncertainty_data_multi_level():
+    prob = [
+        np.array([[0.88, 0.06, 0.06], [0.22, 0.48, 0.30], [0.33, 0.33, 0.34]]),
+        np.array([[0.88, 0.06, 0.06], [0.22, 0.48, 0.30], [0.33, 0.33, 0.34]]),
+    ]
+
+    assert_array_equal(np.sum(prob[0], axis=1), np.ones(len(prob[0])))
+
+    y_pred = np.array([[0, 3], [1, 4], [2, 5]])
+    y_true = np.array([[0, 3], [2, 5], [0, 4]])
+
+    return prob, y_pred, y_true
+
+
+def test_local_brier_score(uncertainty_data):
+    prob, _, y_true = uncertainty_data
+    obj = HierarchicalClassifier()
+    classifier = Mock(spec=obj)
+    classifier._disambiguate = obj._disambiguate
+    classifier.classes_ = [[0, 1, 2]]
+    classifier.separator_ = "::HiClass::Separator::"
+
+    brier_score = _multiclass_brier_score(classifier, y_true, prob[0], level=0)
+    assert math.isclose(brier_score, 0.34852, abs_tol=1e-4)
+
+
+def test_brier_score_multi_level(uncertainty_data_multi_level):
+    prob, _, y_true = uncertainty_data_multi_level
+    obj = HierarchicalClassifier()
+    classifier = Mock(spec=obj)
+    classifier._disambiguate = obj._disambiguate
+    classifier.classes_ = [[0, 1, 2], [3, 4, 5]]
+    classifier.separator_ = "::HiClass::Separator::"
+    brier_score_avg = multiclass_brier_score(classifier, y_true, prob, agg="average")
+    brier_score_sum = multiclass_brier_score(classifier, y_true, prob, agg="sum")
+    brier_score_per_level = multiclass_brier_score(classifier, y_true, prob, agg=None)
+    assert math.isclose(brier_score_avg, 0.48793, abs_tol=1e-4)
+    assert math.isclose(brier_score_sum, 0.97586, abs_tol=1e-4)
+    assert math.isclose(brier_score_per_level[0], 0.48793, abs_tol=1e-4)
+    assert math.isclose(brier_score_per_level[1], 0.48793, abs_tol=1e-4)
+
+
+def test_brier_score_single_level(uncertainty_data_multi_level):
+    prob, _, y_true = uncertainty_data_multi_level
+    prob = prob[1]
+    obj = HierarchicalClassifier()
+    classifier = Mock(spec=obj)
+    classifier._disambiguate = obj._disambiguate
+    classifier.classes_ = [[0, 1, 2], [3, 4, 5]]
+    classifier.separator_ = "::HiClass::Separator::"
+
+    brier_score_1 = multiclass_brier_score(classifier, y_true, prob, level=1)
+    brier_score_2 = multiclass_brier_score(classifier, y_true, [prob], level=1)
+    assert math.isclose(brier_score_1, 0.48793, abs_tol=1e-4)
+    assert math.isclose(brier_score_2, 0.48793, abs_tol=1e-4)
+
+
+def test_local_log_loss(uncertainty_data):
+    prob, _, y_true = uncertainty_data
+    obj = HierarchicalClassifier()
+    classifier = Mock(spec=obj)
+    classifier._disambiguate = obj._disambiguate
+    classifier.classes_ = [[0, 1, 2]]
+    classifier.separator_ = "::HiClass::Separator::"
+
+    log_loss = _log_loss(classifier, y_true, prob[0], level=0)
+    assert math.isclose(log_loss, 0.61790, abs_tol=1e-4)
+
+
+def test_log_loss_multi_level(uncertainty_data_multi_level):
+    prob, _, y_true = uncertainty_data_multi_level
+    obj = HierarchicalClassifier()
+    classifier = Mock(spec=obj)
+    classifier._disambiguate = obj._disambiguate
+    classifier.classes_ = [[0, 1, 2], [3, 4, 5]]
+    classifier.separator_ = "::HiClass::Separator::"
+    log_loss_avg = log_loss(classifier, y_true, prob, agg="average")
+    log_loss_sum = log_loss(classifier, y_true, prob, agg="sum")
+    log_loss_per_level = log_loss(classifier, y_true, prob, agg=None)
+    assert math.isclose(log_loss_avg, 0.81348, abs_tol=1e-4)
+    assert math.isclose(log_loss_sum, 1.62697, abs_tol=1e-4)
+    assert math.isclose(log_loss_per_level[0], 0.81348, abs_tol=1e-4)
+    assert math.isclose(log_loss_per_level[1], 0.81348, abs_tol=1e-4)
+
+
+def test_log_loss_single_level(uncertainty_data_multi_level):
+    prob, _, y_true = uncertainty_data_multi_level
+    prob = prob[1]
+    obj = HierarchicalClassifier()
+    classifier = Mock(spec=obj)
+    classifier._disambiguate = obj._disambiguate
+    classifier.classes_ = [[0, 1, 2], [3, 4, 5]]
+    classifier.separator_ = "::HiClass::Separator::"
+
+    log_loss_1 = log_loss(classifier, y_true, prob, level=1)
+    log_loss_2 = log_loss(classifier, y_true, [prob], level=1)
+    assert math.isclose(log_loss_1, 0.81349, abs_tol=1e-4)
+    assert math.isclose(log_loss_2, 0.81348, abs_tol=1e-4)
+
+
+def test_local_expected_calibration_error(uncertainty_data):
+    prob, y_pred, y_true = uncertainty_data
+    obj = HierarchicalClassifier()
+    classifier = Mock(spec=obj)
+    classifier._disambiguate = obj._disambiguate
+    classifier.classes_ = [[0, 1, 2]]
+    classifier.separator_ = "::HiClass::Separator::"
+
+    ece = _expected_calibration_error(
+        classifier, y_true, prob[0], y_pred, level=0, n_bins=3
+    )
+    assert math.isclose(ece, 0.118, abs_tol=1e-4)
+
+
+def test_expected_calibration_error_multi_level(uncertainty_data_multi_level):
+    prob, y_pred, y_true = uncertainty_data_multi_level
+    obj = HierarchicalClassifier()
+    classifier = Mock(spec=obj)
+    classifier._disambiguate = obj._disambiguate
+    classifier.classes_ = [[0, 1, 2], [3, 4, 5]]
+    classifier.separator_ = "::HiClass::Separator::"
+    ece_avg = expected_calibration_error(
+        classifier, y_true, prob, y_pred, agg="average"
+    )
+    ece_sum = expected_calibration_error(classifier, y_true, prob, y_pred, agg="sum")
+    ece_per_level = expected_calibration_error(
+        classifier, y_true, prob, y_pred, agg=None
+    )
+    assert math.isclose(ece_avg, 0.31333, abs_tol=1e-4)
+    assert math.isclose(ece_sum, 0.62666, abs_tol=1e-4)
+    assert math.isclose(ece_per_level[0], 0.31333, abs_tol=1e-4)
+    assert math.isclose(ece_per_level[1], 0.31333, abs_tol=1e-4)
+
+
+def test_expected_calibration_error_single_level(uncertainty_data_multi_level):
+    prob, y_pred, y_true = uncertainty_data_multi_level
+    prob = prob[1]
+    obj = HierarchicalClassifier()
+    classifier = Mock(spec=obj)
+    classifier._disambiguate = obj._disambiguate
+    classifier.classes_ = [[0, 1, 2], [3, 4, 5]]
+    classifier.separator_ = "::HiClass::Separator::"
+    ece_1 = expected_calibration_error(classifier, y_true, prob, y_pred, level=1)
+    ece_2 = expected_calibration_error(classifier, y_true, [prob], y_pred, level=1)
+    assert math.isclose(ece_1, 0.31333, abs_tol=1e-4)
+    assert math.isclose(ece_2, 0.31333, abs_tol=1e-4)
+
+
+def test_local_static_calibration_error(uncertainty_data):
+    prob, y_pred, y_true = uncertainty_data
+    obj = HierarchicalClassifier()
+    classifier = Mock(spec=obj)
+    classifier._disambiguate = obj._disambiguate
+    classifier.classes_ = [[0, 1, 2]]
+    classifier.separator_ = "::HiClass::Separator::"
+
+    sce = _static_calibration_error(
+        classifier, y_true, prob[0], y_pred, level=0, n_bins=3
+    )
+    assert math.isclose(sce, 0.3889, abs_tol=1e-3)
+
+
+def test_static_calibration_error_multi_level(uncertainty_data_multi_level):
+    prob, y_pred, y_true = uncertainty_data_multi_level
+    obj = HierarchicalClassifier()
+    classifier = Mock(spec=obj)
+    classifier._disambiguate = obj._disambiguate
+    classifier.classes_ = [[0, 1, 2], [3, 4, 5]]
+    classifier.separator_ = "::HiClass::Separator::"
+
+    sce_avg = static_calibration_error(classifier, y_true, prob, y_pred, agg="average")
+    sce_sum = static_calibration_error(classifier, y_true, prob, y_pred, agg="sum")
+    sce_per_level = static_calibration_error(classifier, y_true, prob, y_pred, agg=None)
+    assert math.isclose(sce_avg, 0.44444, abs_tol=1e-4)
+    assert math.isclose(sce_sum, 0.88888, abs_tol=1e-4)
+    assert math.isclose(sce_per_level[0], 0.44444, abs_tol=1e-4)
+    assert math.isclose(sce_per_level[1], 0.44444, abs_tol=1e-4)
+
+
+def test_static_calibration_error_single_level(uncertainty_data_multi_level):
+    prob, y_pred, y_true = uncertainty_data_multi_level
+    prob = prob[1]
+    obj = HierarchicalClassifier()
+    classifier = Mock(spec=obj)
+    classifier._disambiguate = obj._disambiguate
+    classifier.classes_ = [[0, 1, 2], [3, 4, 5]]
+    classifier.separator_ = "::HiClass::Separator::"
+    sce_1 = static_calibration_error(classifier, y_true, prob, y_pred, level=1)
+    sce_2 = static_calibration_error(classifier, y_true, [prob], y_pred, level=1)
+    assert math.isclose(sce_1, 0.44444, abs_tol=1e-4)
+    assert math.isclose(sce_2, 0.44444, abs_tol=1e-4)
+
+
+def test_local_adaptive_calibration_error(uncertainty_data):
+    prob, y_pred, y_true = uncertainty_data
+    obj = HierarchicalClassifier()
+    classifier = Mock(spec=obj)
+    classifier._disambiguate = obj._disambiguate
+    classifier.classes_ = [[0, 1, 2]]
+    classifier.separator_ = "::HiClass::Separator::"
+
+    ace = _adaptive_calibration_error(
+        classifier, y_true, prob[0], y_pred, level=0, n_ranges=3
+    )
+    assert math.isclose(ace, 0.44, abs_tol=1e-3)
+
+
+def test_adaptive_calibration_error_multi_level(uncertainty_data_multi_level):
+    prob, y_pred, y_true = uncertainty_data_multi_level
+    obj = HierarchicalClassifier()
+    classifier = Mock(spec=obj)
+    classifier._disambiguate = obj._disambiguate
+    classifier.classes_ = [[0, 1, 2], [3, 4, 5]]
+    classifier.separator_ = "::HiClass::Separator::"
+
+    ace_avg = adaptive_calibration_error(
+        classifier, y_true, prob, y_pred, agg="average"
+    )
+    ace_sum = adaptive_calibration_error(classifier, y_true, prob, y_pred, agg="sum")
+    ace_per_level = adaptive_calibration_error(
+        classifier, y_true, prob, y_pred, agg=None
+    )
+    assert math.isclose(ace_avg, 0.13333, abs_tol=1e-4)
+    assert math.isclose(ace_sum, 0.26666, abs_tol=1e-4)
+    assert math.isclose(ace_per_level[0], 0.13333, abs_tol=1e-4)
+    assert math.isclose(ace_per_level[1], 0.13333, abs_tol=1e-4)
+
+
+def test_adaptive_calibration_error_single_level(uncertainty_data_multi_level):
+    prob, y_pred, y_true = uncertainty_data_multi_level
+    prob = prob[1]
+    obj = HierarchicalClassifier()
+    classifier = Mock(spec=obj)
+    classifier._disambiguate = obj._disambiguate
+    classifier.classes_ = [[0, 1, 2], [3, 4, 5]]
+    classifier.separator_ = "::HiClass::Separator::"
+
+    ace_1 = adaptive_calibration_error(classifier, y_true, prob, y_pred, level=1)
+    ace_2 = adaptive_calibration_error(classifier, y_true, [prob], y_pred, level=1)
+    assert math.isclose(ace_1, 0.13333, abs_tol=1e-4)
+    assert math.isclose(ace_2, 0.13333, abs_tol=1e-4)