diff --git a/examples/scripts/binary_classifier.py b/examples/scripts/binary_classifier.py
index e4aff407..3eab9bdc 100644
--- a/examples/scripts/binary_classifier.py
+++ b/examples/scripts/binary_classifier.py
@@ -20,11 +20,12 @@
 
 automl = AutoML(
     algorithms=["LightGBM"],
-    mode="Perform",
+    mode="Compete",
     explain_level=0,
-    train_ensemble=False,
+    train_ensemble=True,
     golden_features=False,
     features_selection=False,
+    eval_metric="auc"
 )
 automl.fit(X_train, y_train)
 
diff --git a/supervised/algorithms/catboost.py b/supervised/algorithms/catboost.py
index cb4bdbd8..d02b0bac 100644
--- a/supervised/algorithms/catboost.py
+++ b/supervised/algorithms/catboost.py
@@ -159,7 +159,7 @@ def file_extension(self):
     "rsm": [0.7, 0.8, 0.9, 1],  # random subspace method
     "subsample": [0.7, 0.8, 0.9, 1],  # random subspace method
     "min_data_in_leaf": [1, 5, 10, 15, 20, 30, 50],
-    "loss_function": ["Logloss"]
+    "loss_function": ["Logloss"],
 }
 
 classification_default_params = {
@@ -167,8 +167,8 @@ def file_extension(self):
     "depth": 6,
     "rsm": 0.9,
     "subsample": 1.0,
-    "min_data_in_leaf": 15,    
-    "loss_function": "Logloss"
+    "min_data_in_leaf": 15,
+    "loss_function": "Logloss",
 }
 
 additional = {
@@ -226,7 +226,7 @@ def file_extension(self):
     "rsm": 0.9,
     "subsample": 1.0,
     "min_data_in_leaf": 15,
-    "loss_function": "RMSE"
+    "loss_function": "RMSE",
 }
 
 AlgorithmsRegistry.add(
diff --git a/supervised/base_automl.py b/supervised/base_automl.py
index d4e436e8..e1b9acb8 100644
--- a/supervised/base_automl.py
+++ b/supervised/base_automl.py
@@ -1300,13 +1300,21 @@ def _validate_eval_metric(self):
         if isinstance(self.eval_metric, str) and self.eval_metric == "auto":
             return
 
-        if (
-            self._get_ml_task() == BINARY_CLASSIFICATION
-            or self._get_ml_task() == MULTICLASS_CLASSIFICATION
+        if (self._get_ml_task() == BINARY_CLASSIFICATION) and self.eval_metric not in [
+            "logloss",
+            "auc",
+        ]:
+            raise ValueError(
+                f"Metric {self.eval_metric} is not allowed in ML task: {self._get_ml_task()}. \
+                    Use 'logloss'"
+            )
+
+        elif (
+            self._get_ml_task() == MULTICLASS_CLASSIFICATION
         ) and self.eval_metric != "logloss":
             raise ValueError(
                 f"Metric {self.eval_metric} is not allowed in ML task: {self._get_ml_task()}. \
-                    Use 'log_loss'"
+                    Use 'logloss'"
             )
 
         elif self._get_ml_task() == REGRESSION and self.eval_metric != "rmse":
diff --git a/supervised/utils/metric.py b/supervised/utils/metric.py
index ebbff889..f4047099 100644
--- a/supervised/utils/metric.py
+++ b/supervised/utils/metric.py
@@ -30,6 +30,11 @@ def rmse(y_true, y_predicted):
     return np.sqrt(val) if val > 0 else -np.Inf
 
 
+def negative_auc(y_true, y_predicted):
+    val = roc_auc_score(y_true, y_predicted)
+    return -1.0 * val
+
+
 class Metric(object):
     def __init__(self, params):
         if params is None:
@@ -38,11 +43,18 @@ def __init__(self, params):
         self.name = self.params.get("name")
         if self.name is None:
             raise MetricException("Metric name not defined")
-        self.minimize_direction = self.name in ["logloss", "rmse", "mae", "ce", "mse"]
+        self.minimize_direction = self.name in [
+            "logloss",
+            "rmse",
+            "mae",
+            "ce",
+            "mse",
+            "auc",
+        ]
         if self.name == "logloss":
             self.metric = logloss
         elif self.name == "auc":
-            self.metric = roc_auc_score
+            self.metric = negative_auc
         elif self.name == "acc":
             self.metric = accuracy_score
         elif self.name == "rmse":