Description tidyRF The featureContrib and trainsetBias families The MDI and MDIoob families Examples
An R re-implementation of the 'treeinterpreter' package on PyPI. <https://pypi.org/project/treeinterpreter/>. Each prediction can be decomposed as 'prediction = bias + feature_1_contribution + ... + feature_n_contribution'. This decomposition is then used to calculate the Mean Decrease Impurity (MDI) and Mean Decrease Impurity using out-of-bag samples (MDI-oob) feature importance measures based on the work of Li et al. (2019) <arXiv:1906.10845>.
tidyRF
The function tidyRF
can turn a randomForest
or ranger
object into a package-agnostic random forest object. All other functions
in this package operate on such a tidyRF
object.
featureContrib
and trainsetBias
familiesThe featureContrib
and trainsetBias
families can decompose the
prediction of regression/classification trees/forests into bias and feature
contribution components.
MDI
and MDIoob
familiesThe MDI
family can calculate the good old MDI feature importance
measure, which unfortunately has some feature selection bias. MDI-oob is a
debiased MDI feature importance measure that has achieved state-of-the-art
performance in feature selection for both simulated and real data. It can be
calculated with functions from the MDIoob
family.
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