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Software . 2021
Data sources: Datacite
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Interactive-Decision-Trees

Authors: Sarailidis, Georgios; Wagener, Thorsten; Pianosi, Francesca;
Abstract

This is the first release of Interactive Decision Trees package. It contains Python modules that enable the user to interact with the Decision Tree by creating new composite variables, grouping the input variables and color code the groups, selecting important variables, manually change variable and threshold to split, manually change leaf node class and manually prune the Decision Tree. Moreover, it contains a graphical user interface in Jupyter Lab notebook for supporting the user-decision tree interactions.

Related Organizations
Keywords

Human in the loop, Interactive Machine Learning, Interpretability, Interactive Decision Trees

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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