gregtam / Interpreting Decision Trees And Random Forests
Unwrapping decision trees and random forests to make them less of a black box
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interpreting-decision-trees-and-random-forests
In this repository, we explore how to understand decision trees and random forests better. This serves to provide additional interpretation beyond just the feature importances. We do this by using the treeinterpreter package at https://github.com/andosa/treeinterpreter. This package looks at the individual contribution of each feature for a given example.
Links to his blog:
- http://blog.datadive.net/interpreting-random-forests/
- http://blog.datadive.net/random-forest-interpretation-with-scikit-learn/
- http://blog.datadive.net/random-forest-interpretation-conditional-feature-contributions/
View my blog on this topic here.
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