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drf (version 1.1.0)

Distributional Random Forests

Description

An implementation of distributional random forests as introduced in Cevid & Michel & Meinshausen & Buhlmann (2020) .

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Version

Install

install.packages('drf')

Monthly Downloads

183

Version

1.1.0

License

GPL-3

Issues

16

Pull Requests

2

Stars

43

Forks

10

Maintainer

Loris Michel

Last Published

March 29th, 2021

Functions in drf (1.1.0)

plot.drf_tree

Plot a DRF tree object.
medianHeuristic

Compute the median heuristic for the MMD bandwidth choice
get_sample_weights

Given a trained forest and test data, compute the training sample weights for each test point.
predict.drf

Predict with a drf forest
get_tree

Retrieve a single tree from a trained forest object.
create_dot_body

Writes each node information If it is a leaf node: show it in different color, show number of samples, show leaf id If it is a non-leaf node: show its splitting variable and splitting value
drf

Distributional Random Forests
leaf_stats.drf

Calculate summary stats given a set of samples for regression forests.
leaf_stats.default

A default leaf_stats for forests classes without a leaf_stats method that always returns NULL.
export_graphviz

Export a tree in DOT format. This function generates a GraphViz representation of the tree, which is then written into `dot_string`.
print.drf

Print a DRF forest object.
print.drf_tree

Print a DRF tree object.
variable_importance

Calculate a simple measure of 'importance' for each feature.
variableImportance

Variable importance based on MMD
split_frequencies

Calculate which features the forest split on at each depth.
weighted.quantile

Weighted quantiles