DOC add more details regarding effect of kind_sel in ENN

Author: glemaitre

doc/under_sampling.rst

@@ -244,10 +244,7 @@ Edited data set using nearest neighbours
 "edit" the dataset by removing samples which do not agree "enough" with their
 neighboorhood :cite:`wilson1972asymptotic`. For each sample in the class to be
 under-sampled, the nearest-neighbours are computed and if the selection
-criterion is not fulfilled, the sample is removed. Two selection criteria are
-currently available: (i) the majority (i.e., ``kind_sel='mode'``) or (ii) all
-(i.e., ``kind_sel='all'``) the nearest-neighbors have to belong to the same
-class than the sample inspected to keep it in the dataset::
+criterion is not fulfilled, the sample is removed::
 
   >>> sorted(Counter(y).items())
   [(0, 64), (1, 262), (2, 4674)]
@@ -257,6 +254,22 @@ class than the sample inspected to keep it in the dataset::
   >>> print(sorted(Counter(y_resampled).items()))
   [(0, 64), (1, 213), (2, 4568)]
 
+Two selection criteria are currently available: (i) the majority (i.e.,
+``kind_sel='mode'``) or (ii) all (i.e., ``kind_sel='all'``) the
+nearest-neighbors have to belong to the same class than the sample inspected to
+keep it in the dataset. Thus, it implies that `kind_sel='all'` will be less
+conservative than `kind_sel='mode'`, and more samples will be excluded in
+the former strategy than the latest::
+
+  >>> enn = EditedNearestNeighbours(kind_sel="all")
+  >>> X_resampled, y_resampled = enn.fit_resample(X, y)
+  >>> print(sorted(Counter(y_resampled).items()))
+  [(0, 64), (1, 213), (2, 4568)]
+  >>> enn = EditedNearestNeighbours(kind_sel="mode")
+  >>> X_resampled, y_resampled = enn.fit_resample(X, y)
+  >>> print(sorted(Counter(y_resampled).items()))
+  [(0, 64), (1, 234), (2, 4666)]
+
 The parameter ``n_neighbors`` allows to give a classifier subclassed from
 ``KNeighborsMixin`` from scikit-learn to find the nearest neighbors and make
 the decision to keep a given sample or not.

imblearn/under_sampling/_prototype_selection/_edited_nearest_neighbours.py

@@ -52,6 +52,9 @@ class EditedNearestNeighbours(BaseCleaningSampler):
         - If ``'mode'``, the majority vote of the neighbours will be used in
           order to exclude a sample.
 
+        The strategy `"all"` will be less conservative than `'mode'`. Thus,
+        more samples will be removed when `kind_sel="all"` generally.
+
     {n_jobs}
 
     Attributes
@@ -195,6 +198,9 @@ class RepeatedEditedNearestNeighbours(BaseCleaningSampler):
         - If ``'mode'``, the majority vote of the neighbours will be used in
           order to exclude a sample.
 
+        The strategy `"all"` will be less conservative than `'mode'`. Thus,
+        more samples will be removed when `kind_sel="all"` generally.
+
     {n_jobs}
 
     Attributes
@@ -373,6 +379,9 @@ class AllKNN(BaseCleaningSampler):
         - If ``'mode'``, the majority vote of the neighbours will be used in
           order to exclude a sample.
 
+        The strategy `"all"` will be less conservative than `'mode'`. Thus,
+        more samples will be removed when `kind_sel="all"` generally.
+
     allow_minority : bool, default=False
         If ``True``, it allows the majority classes to become the minority
         class without early stopping.

imblearn/under_sampling/_prototype_selection/_neighbourhood_cleaning_rule.py

@@ -50,6 +50,9 @@ class NeighbourhoodCleaningRule(BaseCleaningSampler):
         - If ``'mode'``, the majority vote of the neighbours will be used in
           order to exclude a sample.
 
+        The strategy `"all"` will be less conservative than `'mode'`. Thus,
+        more samples will be removed when `kind_sel="all"` generally.
+
     threshold_cleaning : float, default=0.5
         Threshold used to whether consider a class or not during the cleaning
         after applying ENN. A class will be considered during cleaning when: