diff --git a/problem.py b/problem.py
index 41bfd0d..9fe1ce2 100644
--- a/problem.py
+++ b/problem.py
@@ -2,6 +2,7 @@
 import os
 import datetime
 
+import warnings
 import numpy as np
 import pandas as pd
 from sklearn.model_selection import KFold
@@ -98,6 +99,26 @@ def y_pred_label_index(self):
         """Multi-class y_pred is the index of the predicted label."""
         return np.argmax(self.y_pred[:, 1:], axis=1)
 
+    @classmethod
+    def combine(cls, predictions_list, index_list=None):
+        if index_list is None:  # we combine the full list
+            index_list = range(len(predictions_list))
+        y_comb_list = np.array(
+            [predictions_list[i].y_pred for i in index_list])
+        # clipping probas into [0, 1], also taking care of the case of all
+        # zeros
+        y_comb_list[:, :, 1:] = np.clip(
+            y_comb_list[:, :, 1:], 10 ** -15, 1 - 10 ** -15)
+        # normalizing probabilities
+        y_comb_list[:, :, 1:] = y_comb_list[:, :, 1:] / np.sum(
+            y_comb_list[:, :, 1:], axis=2, keepdims=True)
+        # I expect to see RuntimeWarnings in this block
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore', category=RuntimeWarning)
+            y_comb = np.nanmean(y_comb_list, axis=0)
+        combined_predictions = cls(y_pred=y_comb)
+        return combined_predictions
+
 
 # -----------------------------------------------------------------------------
 # Score types
@@ -355,7 +376,7 @@ def turn_prediction_to_event_list(y, thres=0.5):
 
 
 score_types = [
-    # log-loss
+    # mixed log-loss/f1 score
     Mixed(),
     # log-loss
     PointwiseLogLoss(),
@@ -364,10 +385,9 @@ def turn_prediction_to_event_list(y, thres=0.5):
     PointwiseRecall(),
     # event-based precision and recall
     EventwisePrecision(),
-    EventwiseRecall()
+    EventwiseRecall(),
 ]
 
-
 # -----------------------------------------------------------------------------
 # Cross-validation scheme
 # -----------------------------------------------------------------------------