Improve documentation

README.md

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 [![pytest](https://github.com/ClaudioSalvatoreArcidiacono/felimination/workflows/Tests/badge.svg)](https://github.com/ClaudioSalvatoreArcidiacono/felimination/actions?query=workflow%3A%22Tests%22)
 [![PyPI](https://img.shields.io/pypi/v/felimination)](#)
+[![documentation](https://img.shields.io/badge/docs-mkdocs%20material-blue.svg?style=flat)](https://claudiosalvatorearcidiacono.github.io/felimination/)
 
 
 # felimination
 
 This library contains some useful scikit-learn compatible classes for feature selection.
 
-## [Check out documentation here](https://claudiosalvatorearcidiacono.github.io/felimination/)
-
 ## Features
 
 - [Recursive Feature Elimination with Cross Validation using Permutation Importance](reference/RFE.md#felimination.rfe.PermutationImportanceRFECV)

docs/reference/RFE.md

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 ::: felimination.rfe
+    options:
+        inherited_members: true

docs/reference/drift.md

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+::: felimination.drift
+    options:
+        inherited_members: true

felimination/drift.py

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-"""Module with tools to perform drift-based feature selection.
+"""The idea behind this module comes from the conjunction of two concepts:
+
+- [1] [Classifier Two-Sample Test](https://arxiv.org/abs/1610.06545)
+- [2] [Recursive Feature Elimination](\
+    https://scikit-learn.org/stable/modules/generated/\
+    sklearn.feature_selection.RFE.html)
+
+In [1] classifier performances are used to determine how similar two samples are. More
+specifically, imagine to have two samples: `reference` and `test`. In order to assess
+whether `reference` and `test` have been drawn from the same distribution, we could
+train a classifier in classifying which instances belong to which sample. If the
+model easily distinguishes instances from the two samples, then the two samples
+have been probably drawn from two different distributions. Conversely, if the
+classifier struggles to distinguish them, then it is likely that the samples have
+been drawn from the same distribution.
+
+In the context of drift detection, the classifier two-sample test can be used to
+assess whether drift has happened between the reference and the test set and to
+which degree.
+
+The classes of this module take this idea one step further and attempt
+to reduce the drift using recursive feature selection. After a classifier
+is trained to distinguish between `reference` and `test`, the feature
+importance of the classifier is used to determine which features contribute
+the most in distinguishing between the two sets. The most important features
+are then eliminated and the procedure is repeated until the classifier is not
+able anymore to distinguish between the two samples, or until a certain amount
+of features has been removed.
 
 This module contains the following classes:
 - `SampleSimilarityDriftRFE`: base class for drift-based sample similarity

mkdocs.yml

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       python:
         options:
           docstring_style: numpy
+        import:
+        - https://scikit-learn.org/stable/objects.inv
 
 markdown_extensions:
   - pymdownx.highlight: