Description Usage Arguments Details Value Functions References See Also Examples
Calculate the MDI feature importance measure.
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tidy.RF |
A tidy random forest. The random forest to calculate MDI from. |
tree |
An integer. The index of the tree to look at. |
trainX |
A data frame. Train set features, such that the |
trainY |
A data frame. Train set responses, such that the |
MDI stands for Mean Decrease in Impurity. It is a widely adopted measure of feature importance in random forests. In this package, we calculate MDI with a new analytical expression derived by Li et al. (See references)
See vignette('MDI', package='tree.interpreter')
for more context.
A matrix. The content depends on the type of the response.
Regression: A P-by-1 matrix, where P is the number of features in
X
. The pth row contains the MDI of feature p.
Classification: A P-by-D matrix, where P is the number of features
in X
and D is the number of response classes. The dth column of
the pth row contains the MDI of feature p to class d. You can get the
MDI of each feature by calling rowSums
on the result.
MDITree
: Mean decrease in impurity within a single tree
MDI
: Mean decrease in impurity within the whole forest
A Debiased MDI Feature Importance Measure for Random Forests https://arxiv.org/abs/1906.10845
vignette('MDI', package='tree.interpreter')
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