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Jul 11, 2023
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DOC improve introduction to undersampling methods #1018

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reword introduction to undersampling methods
solegalli Jul 10, 2023
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solegalli Jul 10, 2023
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solegalli Jul 11, 2023
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solegalli committed Jul 11, 2023
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9 changes: 5 additions & 4 deletions doc/under_sampling.rst
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Expand Up @@ -7,8 +7,9 @@ Under-sampling
.. currentmodule:: imblearn.under_sampling

One way of handling imbalanced datasets is to reduce the number of observations from
the majority class or classes. The most well known algorithm in this group is random
undersampling, where samples from the majority classes are removed at random.
all classes but the minority class. The minority class is that with the least number
of observations. The most well known algorithm in this group is random
undersampling, where samples from the targeted classes are removed at random.

But there are many other algorithms to help us reduce the number of observations in the
dataset. These algorithms can be grouped based on their undersampling strategy into:
Expand All @@ -23,8 +24,8 @@ And within the latter, we find:

We will discuss the different algorithms throughout this document.

Refer to :ref:`sphx_glr_auto_examples_under-sampling_plot_comparison_under_sampling.py`
for a comparison of the different undersampling methodologies.
Check also
:ref:`sphx_glr_auto_examples_under-sampling_plot_comparison_under_sampling.py`.

.. _cluster_centroids:

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