DOC fix issues with SMOTEN doc

Author: glemaitre

README.rst

@@ -167,8 +167,8 @@ Below is a list of the methods currently implemented in this module.
 * Over-sampling
     1. Random minority over-sampling with replacement
     2. SMOTE - Synthetic Minority Over-sampling Technique [8]_
-    3. SMOTENC - SMOTE for Nominal Continuous [8]_
-    4. SMOTEN - SMMOTE for Nominal only [8]_
+    3. SMOTENC - SMOTE for Nominal and Continuous [8]_
+    4. SMOTEN - SMOTE for Nominal [8]_
     5. bSMOTE(1 & 2) - Borderline SMOTE of types 1 and 2 [9]_
     6. SVM SMOTE - Support Vectors SMOTE [10]_
     7. ADASYN - Adaptive synthetic sampling approach for imbalanced learning [15]_

doc/over_sampling.rst

@@ -211,16 +211,16 @@ Therefore, it can be seen that the samples generated in the first and last
 columns are belonging to the same categories originally presented without any
 other extra interpolation.
 
-However, :class:`SMOTENC` is working with data composed of categorical data
-only. WHen data are made of only nominal categorical data, one can use the
-:class:`SMOTEN` variant :cite:`chawla2002smote`. The algorithm changes in
-two ways:
+However, :class:`SMOTENC` is only working when data is a mixed of numerical and
+categorical features. If data are made of only nominal categorical data, one
+can use the :class:`SMOTEN` variant :cite:`chawla2002smote`. The algorithm
+changes in two ways:
 
 * the nearest neighbors search does not rely on the Euclidean distance. Indeed,
   the value difference metric (VDM) also implemented in the class
   :class:`~imblearn.metrics.ValueDifferenceMetric` is used.
-* the new sample generation is based on majority vote per feature to generate
-  the most common category seen in the neighbors samples.
+* a new sample is generated where each feature value corresponds to the most
+  common category seen in the neighbors samples belonging to the same class.
 
 Let's take the following example::
 

imblearn/over_sampling/_adasyn.py

@@ -52,7 +52,7 @@ class ADASYN(BaseOverSampler):
 
     SMOTENC : Over-sample using SMOTE for continuous and categorical features.
 
-    SMOTEN : Over-sample using the SMOTE variable specifically for categorical
+    SMOTEN : Over-sample using the SMOTE variant specifically for nominal
         features only.
 
     SVMSMOTE : Over-sample using SVM-SMOTE variant.

imblearn/over_sampling/_random_over_sampler.py

@@ -76,7 +76,7 @@ class RandomOverSampler(BaseOverSampler):
 
     SMOTENC : Over-sample using SMOTE for continuous and categorical features.
 
-    SMOTEN : Over-sample using the SMOTE variable specifically for categorical
+    SMOTEN : Over-sample using the SMOTE variant specifically for nominal
         features only.
 
     SVMSMOTE : Over-sample using SVM-SMOTE variant.

imblearn/over_sampling/_smote.py

@@ -450,7 +450,7 @@ class SVMSMOTE(BaseSMOTE):
 
     SMOTENC : Over-sample using SMOTE for continuous and categorical features.
 
-    SMOTEN : Over-sample using the SMOTE variable specifically for categorical
+    SMOTEN : Over-sample using the SMOTE variant specifically for nominal
         features only.
 
     BorderlineSMOTE : Over-sample using Borderline-SMOTE.
@@ -648,7 +648,7 @@ class SMOTE(BaseSMOTE):
     --------
     SMOTENC : Over-sample using SMOTE for continuous and categorical features.
 
-    SMOTEN : Over-sample using the SMOTE variable specifically for categorical
+    SMOTEN : Over-sample using the SMOTE variant specifically for nominal
         features only.
 
     BorderlineSMOTE : Over-sample using the borderline-SMOTE variant.
@@ -774,7 +774,7 @@ class SMOTENC(SMOTE):
     --------
     SMOTE : Over-sample using SMOTE.
 
-    SMOTEN : Over-sample using the SMOTE variable specifically for categorical
+    SMOTEN : Over-sample using the SMOTE variant specifically for nominal
         features only.
 
     SVMSMOTE : Over-sample using SVM-SMOTE variant.
@@ -1068,7 +1068,7 @@ class KMeansSMOTE(BaseSMOTE):
 
     SMOTENC : Over-sample using SMOTE for continuous and categorical features.
 
-    SMOTEN : Over-sample using the SMOTE variable specifically for categorical
+    SMOTEN : Over-sample using the SMOTE variant specifically for nominal
         features only.
 
     SVMSMOTE : Over-sample using SVM-SMOTE variant.