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Sup cat features (#14)

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* support categorical features

* update the documentation and readme
added a test for the case where cells are supplied as a param.

* add big tests (adult test and iris)
and fixed bugs

* update transform to return numpy if original data is numpy

* added nursery test

* break loop if there is an illegal level

* Stop pruning one step before passing accuracy threshold

* adding asserts and fix DecisionTreeClassifier init

* Fix tests

Co-authored-by: abigailt <abigailt@il.ibm.com>
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olasaadi 2022-01-11 09:51:04 +02:00 • committed by GitHub
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@ -32,8 +32,7 @@ them to new data.
It is also possible to export the generalizations as feature ranges.
The current implementation supports only numeric features, so any categorical features must be transformed to a numeric
representation before using this class.
The current implementation supports numeric features and categorical features.
Start by training your machine learning model. In this example, we will use a ``DecisionTreeClassifier``, but any
scikit-learn model can be used. We will use the iris dataset in our example.