Deducing neighborhoods of classes from a fitted model
- URL: http://arxiv.org/abs/2009.05516v2
- Date: Thu, 17 Sep 2020 09:47:20 GMT
- Title: Deducing neighborhoods of classes from a fitted model
- Authors: Alexander Gerharz, Andreas Groll, Gunther Schauberger
- Abstract summary: In this article a new kind of interpretable machine learning method is presented.
It can help to understand the partitioning of the feature space into predicted classes in a classification model using quantile shifts.
Basically, real data points (or specific points of interest) are used and the changes of the prediction after slightly raising or decreasing specific features are observed.
- Score: 68.8204255655161
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In todays world the request for very complex models for huge data sets is
rising steadily. The problem with these models is that by raising the
complexity of the models, it gets much harder to interpret them. The growing
field of \emph{interpretable machine learning} tries to make up for the lack of
interpretability in these complex (or even blackbox-)models by using specific
techniques that can help to understand those models better. In this article a
new kind of interpretable machine learning method is presented, which can help
to understand the partitioning of the feature space into predicted classes in a
classification model using quantile shifts. To illustrate in which situations
this quantile shift method (QSM) could become beneficial, it is applied to a
theoretical medical example and a real data example. Basically, real data
points (or specific points of interest) are used and the changes of the
prediction after slightly raising or decreasing specific features are observed.
By comparing the predictions before and after the manipulations, under certain
conditions the observed changes in the predictions can be interpreted as
neighborhoods of the classes with regard to the manipulated features.
Chordgraphs are used to visualize the observed changes.
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