DOC Update What's New v0.6 (#660)

Author: MattEding

doc/whats_new/v0.6.rst

@@ -39,6 +39,7 @@ scikit-learn:
 The following samplers will give different results due to change linked to
 the random state internal usage:
 
+- :class:`imblearn.over_sampling.ADASYN`
 - :class:`imblearn.over_sampling.SMOTENC`
 
 Bug fixes
@@ -100,13 +101,14 @@ Enhancement
   :pr:`644` by :user:`Guillaume Lemaitre <glemaitre>`.
 
 - The samples generation in
+  :class:`imblearn.over_sampling.ADASYN`,
   :class:`imblearn.over_sampling.SMOTE`,
   :class:`imblearn.over_sampling.BorderlineSMOTE`,
   :class:`imblearn.over_sampling.SVMSMOTE`,
   :class:`imblearn.over_sampling.KMeansSMOTE`,
   :class:`imblearn.over_sampling.SMOTENC` is now vectorize with giving
   an additional speed-up when `X` in sparse.
-  :pr:`596` by :user:`Matt Eding <MattEding>`.
+  :pr:`596` and :pr:`649` by :user:`Matt Eding <MattEding>`.
 
 Deprecation
 ...........

imblearn/ensemble/_forest.py

@@ -206,7 +206,7 @@ class BalancedRandomForestClassifier(RandomForestClassifier):
         subtree with the largest cost complexity that is smaller than
         ``ccp_alpha`` will be chosen. By default, no pruning is performed.
 
-        .. versionadded:: 0.22
+        .. versionadded:: 0.6
            Added in `scikit-learn` in 0.22
 
     max_samples : int or float, default=None
@@ -220,7 +220,7 @@ class BalancedRandomForestClassifier(RandomForestClassifier):
         the number of samples given in `max_samples` and the number of samples
         obtained after resampling.
 
-        .. versionadded:: 0.22
+        .. versionadded:: 0.6
            Added in `scikit-learn` in 0.22
 
     Attributes