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commitd53818644eAuthor: olasaadi <92303887+olasaadi@users.noreply.github.com> Date: Mon Mar 7 20:12:55 2022 +0200 Build the dt on all features anon (#23) * add param to build the DT on all features and not just on QI * one-hot encoding only for categorical features commitc47819a031Author: abigailt <abigailt@il.ibm.com> Date: Wed Feb 23 19:40:11 2022 +0200 Update docs commit7e2ce7fe96Merge:7fbd1e4752871dAuthor: abigailt <abigailt@il.ibm.com> Date: Wed Feb 23 19:26:44 2022 +0200 Merge remote-tracking branch 'origin/main' into main commit7fbd1e4b90Author: abigailt <abigailt@il.ibm.com> Date: Wed Feb 23 19:22:54 2022 +0200 Update version and docs commit752871dd0cAuthor: olasaadi <92303887+olasaadi@users.noreply.github.com> Date: Wed Feb 23 14:57:12 2022 +0200 add minimization notebook (#22) * add german credit notebook to showcase new features (minimize only some features and categorical features) * add notebook to show minimization data on a regression problem
16 lines
857 B
Python
16 lines
857 B
Python
"""
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Module providing data minimization for ML.
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This module implements a first-of-a-kind method to help reduce the amount of personal data needed to perform
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predictions with a machine learning model, by removing or generalizing some of the input features. For more information
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about the method see: http://export.arxiv.org/pdf/2008.04113
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The main class, ``GeneralizeToRepresentative``, is a scikit-learn compatible ``Transformer``, that receives an existing
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estimator and labeled training data, and learns the generalizations that can be applied to any newly collected data for
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analysis by the original model. The ``fit()`` method learns the generalizations and the ``transform()`` method applies
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them to new data.
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It is also possible to export the generalizations as feature ranges.
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"""
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from apt.minimization.minimizer import GeneralizeToRepresentative
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