diff --git a/imlightgbm/objective.py b/imlightgbm/objective.py
index 5c3e965..e750786 100644
--- a/imlightgbm/objective.py
+++ b/imlightgbm/objective.py
@@ -6,6 +6,8 @@
 from lightgbm import Dataset
 from sklearn.utils.multiclass import type_of_target
 
+from imlightgbm.base import Metric, Objective, SupportedTask
+
 ObjLike = Callable[[np.ndarray, Dataset], tuple[np.ndarray, np.ndarray]]
 ALPHA_DEFAULT: float = 0.25
 GAMMA_DEFAULT: float = 2.0
@@ -54,8 +56,8 @@ def binary_focal_objective(
     return grad, hess
 
 
-def weighted_binary_cross_entropy(pred: np.ndarray, train_data: Dataset, alpha: float):
-    """Return grad, hess for binary focal objective."""
+def binary_weighted_objective(pred: np.ndarray, train_data: Dataset, alpha: float):
+    """Return grad, hess for binary weighted objective."""
     label = train_data.get_label()
     pred_prob = _sigmoid(pred)
     grad = -(alpha**label) * (label - pred_prob)
@@ -70,41 +72,73 @@ def multiclass_focal_objective(
     return
 
 
-def multiclass_focal_eval(
+def multiclass_weighted_objective(
     pred: np.ndarray, train_data: Dataset, alpha: float, gamma: float
 ) -> tuple[str, float, bool]:
     # TODO
     return
 
 
-def _set_fobj_feval(
+def _get_metric(task_enum: SupportedTask, metric: str | None) -> str:
+    """Retrieve the appropriate metric function based on task."""
+    metric_mapper: dict[SupportedTask, list[Metric]] = {
+        SupportedTask.binary: [Metric.auc, Metric.binary_error, Metric.binary_logloss],
+        SupportedTask.multiclass: [
+            Metric.auc_mu,
+            Metric.multi_logloss,
+            Metric.multi_error,
+        ],
+    }
+    if metric:
+        metric_enum = Metric.get(metric)
+        metric_enums = metric_mapper[task_enum]
+        if metric_enum not in metric_enums:
+            valid_metrics = ", ".join([m.value for m in metric_enums])
+            raise ValueError(f"Invalid metric: Supported metrics are {valid_metrics}")
+        return metric_enum.value
+
+    return metric_mapper[task_enum][0].value
+
+
+def _get_objective(
+    task_enum: SupportedTask, objective: str | None, alpha: float, gamma: float
+) -> ObjLike:
+    """Retrieve the appropriate objective function based on task and objective type."""
+    objective_mapper: dict[SupportedTask, dict[Objective, ObjLike]] = {
+        SupportedTask.binary: {
+            Objective.focal: partial(binary_focal_objective, gamma=gamma),
+            Objective.weighted: partial(binary_weighted_objective, alpha=alpha),
+        },
+        SupportedTask.multiclass: {
+            Objective.focal: partial(
+                multiclass_focal_objective, alpha=alpha, gamma=gamma
+            ),
+            Objective.weighted: partial(
+                multiclass_weighted_objective, alpha=alpha, gamma=gamma
+            ),
+        },
+    }
+    if objective:
+        objective_enum = Objective.get(objective)
+        return objective_mapper[task_enum][objective_enum]
+
+    return objective_mapper[task_enum][Objective.focal]
+
+
+def _get_fobj_feval(
     train_set: Dataset,
     alpha: float,
     gamma: float,
-    objective: str | None = None,
-    metric: str | None = None,
-) -> tuple[ObjLike, list[str]]:
+    objective: str | None,
+    metric: str | None,
+) -> tuple[ObjLike, str]:
     """Return obj and eval with respect to task type."""
-    inferred_task = type_of_target(train_set.get_label())
-    if inferred_task not in {"binary"}:  # TODO: multiclass
-        raise ValueError(
-            f"Invalid target type: {inferred_task}. Supported types is 'binary'."
-        )
-
-    feval = metric if metric else "auc"
-    if objective:
-        if objective not in {"focal", "weighted"}:
-            raise ValueError(
-                f"Invalid objective: {objective}. Supported types is 'focal' and 'weighted'."
-            )
-        objective_mapper: dict[str, ObjLike] = {
-            "focal": partial(binary_focal_objective, gamma=gamma),
-            "weighted": partial(weighted_binary_cross_entropy, alpha=alpha),
-        }
-        fobj = objective_mapper[objective]
-    else:
-        fobj: ObjLike = partial(binary_focal_objective, gamma=gamma)
-
+    _task = type_of_target(train_set.get_label())
+    task_enum = SupportedTask.get(_task)
+    feval = _get_metric(task_enum=task_enum, metric=metric)
+    fobj = _get_objective(
+        task_enum=task_enum, objective=objective, alpha=alpha, gamma=gamma
+    )
     return fobj, feval
 
 
@@ -117,13 +151,10 @@ def set_params(params: dict[str, Any], train_set: Dataset) -> dict[str, Any]:
     if _metric and not isinstance(_metric, str):
         raise ValueError("metric must be str")
 
-    _alpha = _params.pop("alpha", ALPHA_DEFAULT)
-    _gamma = _params.pop("gamma", GAMMA_DEFAULT)
-
-    fobj, feval = _set_fobj_feval(
+    fobj, feval = _get_fobj_feval(
         train_set=train_set,
-        alpha=_alpha,
-        gamma=_gamma,
+        alpha=_params.pop("alpha", ALPHA_DEFAULT),
+        gamma=_params.pop("gamma", GAMMA_DEFAULT),
         objective=_objective,
         metric=_metric,
     )