From 585c18ff3ad4076874cfa3746a7ae5585853b381 Mon Sep 17 00:00:00 2001 From: rasbt Date: Fri, 25 Aug 2017 02:31:49 -0400 Subject: [PATCH 1/2] minor rephrasing in the combine doc --- doc/combine.rst | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/doc/combine.rst b/doc/combine.rst index e4cdc0c23..edd175bca 100644 --- a/doc/combine.rst +++ b/doc/combine.rst @@ -8,19 +8,19 @@ Combination of over- and under-sampling We previously presented :class:`SMOTE` and showed that this method can generate noisy samples by interpolating new points between marginal outliers and -inliers. This issue can be solved by cleaning the resulted space obtained -after over-sampling. +inliers. This issue can be solved by cleaning the space resulting +from over-sampling. .. currentmodule:: imblearn.combine In this regard, Tomek's link and edited nearest-neighbours are the two cleaning -methods which have been added pipeline after SMOTE over-sampling to obtain a -cleaner space. Therefore, imbalanced-learn implemented two ready-to-use class -which pipeline both over- and under-sampling methods: (i) :class:`SMOTETomek` +methods that have been added to the pipeline after applying SMOTE over-sampling +to obtain a cleaner space. The two ready-to use classes imbalanced-learn implements +for combining over- and undersampling methods are: (i) :class:`SMOTETomek` and (ii) :class:`SMOTEENN`. -These two classes can be used as any other sampler with identical parameters -than their former samplers:: +Those two classes can be used like any other sampler with parameters identical +to their former samplers: >>> from collections import Counter >>> from sklearn.datasets import make_classification From 29f0b5cd768a79e82d69cbeed4c62456afbd47c1 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Fri, 25 Aug 2017 10:15:12 +0200 Subject: [PATCH 2/2] Update combine.rst --- doc/combine.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/combine.rst b/doc/combine.rst index edd175bca..165fcc7f0 100644 --- a/doc/combine.rst +++ b/doc/combine.rst @@ -20,7 +20,7 @@ for combining over- and undersampling methods are: (i) :class:`SMOTETomek` and (ii) :class:`SMOTEENN`. Those two classes can be used like any other sampler with parameters identical -to their former samplers: +to their former samplers:: >>> from collections import Counter >>> from sklearn.datasets import make_classification