Fits iterative random forest algorithm. Returns data.frame with variable importances and top rated features. For now this is an internal function that I've used to explore how recursive feature elimination works in simulations. It may be exported at a later time.
iterative_RF(X, y, drop_fraction, keep_fraction, mtry_factor,
ntree_factor = 10, min_ntree = 5000, num_processors = 1, nodesize)A data.frame. Each column corresponds to a feature vectors.
Response vector.
A number between 0 and 1. Percentage of features dropped at each iteration.
A number between 0 and 1. Proportion features from each module to retain at screening step.
A positive number. Mtry for each random forest
is set to
ceiling(\(\sqrt{p}\)mtry_factor)
where p is the current number of features.
A number greater than 1. ntree for each
random is ntree_factor times the number
of features. For each random forest, ntree
is set to max(min_ntree,
ntree_factor*p).
Minimum number of trees grown in each random forest.
Number of processors used to fit random forests.
Minimum nodesize.
A data.frame with the top ranked features.