Description Usage Arguments Details Value Functions References See Also Examples
Calculate the MDI-oob feature importance measure.
1 2 3 | MDIoobTree(tidy.RF, tree, trainX, trainY)
MDIoob(tidy.RF, trainX, trainY)
|
tidy.RF |
A tidy random forest. The random forest to calculate MDI-oob 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 |
It has long been known that MDI incorrectly assigns high importance to noisy features, leading to systematic bias in feature selection. To address this issue, Li et al. proposed a debiased MDI feature importance measure using out-of-bag samples, called MDI-oob, which has achieved state-of-the-art performance in feature selection for both simulated and real data.
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-oob 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-oob of feature p to class d. You can get
the MDI-oob of each feature by calling rowSums
on the result.
MDIoobTree
: Debiased mean decrease in impurity within a single tree
MDIoob
: Debiased 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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