View source: R/MSE_Test_File.R
Runs variable importance for all variables by using a single forest. Traditional random forest works by restricting the features available to split upon in each node in the tree. Holdout forests modify this by instead restricting features available for the entire tree. This induces a natural sorting of variables into trees with and without the variables. Each collection of these trees forms a random forest, which is then run through the MSE Test procedure.
1 2 3 |
X |
Data frame of covariates - the training data. |
y |
Response vector. Currently only numeric responses (regression) are supported. |
X.test |
Covariates of the test set with which the MSE is calculated. |
y.test |
Responses in the test set with which the MSE is calculated. |
base.learner |
One of |
single_forest |
Logical. If |
NTest |
If |
Nbtree |
How many trees should be used for each variable. Can either be a single number, or a vector of length |
verbose |
Logical. Should a progress tracker be output in the console? |
keep_forest |
Logical. Should the original random forest be returned? |
mtry |
The |
p |
Subsample size exponent, see |
... |
Additional arguments to be passed to |
mintree |
|
max.trees |
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