DOC resolve sphinx error

Author: glemaitre

examples/plot_outlier_rejections.py

@@ -88,8 +88,7 @@ def outlier_rejection(X, y):
     """This will be our function used to resample our dataset."""
     model = IsolationForest(max_samples=100,
                             contamination=0.4,
-                            random_state=rng,
-                            behaviour='new')
+                            random_state=rng)
     model.fit(X)
     y_pred = model.predict(X)
     return X[y_pred == 1], y[y_pred == 1]

imblearn/ensemble/_forest.py

@@ -204,8 +204,7 @@ class BalancedRandomForestClassifier(RandomForestClassifier):
     ccp_alpha : non-negative float, optional (default=0.0)
         Complexity parameter used for Minimal Cost-Complexity Pruning. The
         subtree with the largest cost complexity that is smaller than
-        ``ccp_alpha`` will be chosen. By default, no pruning is performed. See
-        :ref:`minimal_cost_complexity_pruning` for details.
+        ``ccp_alpha`` will be chosen. By default, no pruning is performed.
 
         .. versionadded:: 0.22
            Added in `scikit-learn` in 0.22

imblearn/over_sampling/_smote.py

@@ -850,7 +850,7 @@ class SMOTENC(SMOTE):
 
     See
     :ref:`sphx_glr_auto_examples_over-sampling_plot_comparison_over_sampling.py`,
-    and :ref:`sphx_glr_auto_examples_over-sampling_plot_smote.py`.
+    and :ref:`sphx_glr_auto_examples_over-sampling_plot_illustration_generation_sample.py`.
 
     See also
     --------