Rules generation

Generate a relevant subset of Decision Rules based on a Random Forest.

This plugin provides the ability to extract a set of interpretable rules from a Random Forest. The approach is based on the article “Learning accurate and interpretable models based on regularized random forests regression“.

Plugin Information

Version 0.5.6
Author Dataiku (Pierre P & Du P)
Released 2018-08-01
Last updated 2018-08-01

How To Use

You first need to set some general parameters: target, type of prediction, mettric to optimize, performance drop threshold …
Next there are two parameters for the algorithm:

  • Number of clauses in a rule: the longer the rule, the more accurate it is, but the less generalizable it is. Behind the scene, this is the max_depth of the random forest.
  • Regularisation parameters: to filter out noisy rules, we use Lasso. This is the alpha parameter of Lasso.

Finally, using the min support parameter, you can also choose to precise that all rules below a certain support threshold are not interesting.

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