det: Density Estimation With Density Estimation Trees

In mlpack: 'Rcpp' Integration for the 'mlpack' Library

Density Estimation With Density Estimation Trees

Description

An implementation of density estimation trees for the density estimation task. Density estimation trees can be trained or used to predict the density at locations given by query points.

Usage

det(
  folds = NA,
  input_model = NA,
  max_leaf_size = NA,
  min_leaf_size = NA,
  path_format = NA,
  skip_pruning = FALSE,
  test = NA,
  training = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

folds	The number of folds of cross-validation to perform for the estimation (0 is LOOCV. Default value "10" (integer).
input_model	Trained density estimation tree to load (DTree).
max_leaf_size	The maximum size of a leaf in the unpruned, fully grown DET. Default value "10" (integer).
min_leaf_size	The minimum size of a leaf in the unpruned, fully grown DET. Default value "5" (integer).
path_format	The format of path printing: 'lr', 'id-lr', or 'lr-id'. Default value "lr" (character).
skip_pruning	Whether to bypass the pruning process and output the unpruned tree only. Default value "FALSE" (logical).
test	A set of test points to estimate the density of (numeric matrix).
training	The data set on which to build a density estimation tree (numeric matrix).
verbose	Display informational messages and the full list of parameters and timers at the end of execution. Default value "getOption("mlpack.verbose", FALSE)" (logical).

Details

This program performs a number of functions related to Density Estimation Trees. The optimal Density Estimation Tree (DET) can be trained on a set of data (specified by "training") using cross-validation (with number of folds specified with the "folds" parameter). This trained density estimation tree may then be saved with the "output_model" output parameter.

The variable importances (that is, the feature importance values for each dimension) may be saved with the "vi" output parameter, and the density estimates for each training point may be saved with the "training_set_estimates" output parameter.

Enabling path printing for each node outputs the path from the root node to a leaf for each entry in the test set, or training set (if a test set is not provided). Strings like 'LRLRLR' (indicating that traversal went to the left child, then the right child, then the left child, and so forth) will be output. If 'lr-id' or 'id-lr' are given as the "path_format" parameter, then the ID (tag) of every node along the path will be printed after or before the L or R character indicating the direction of traversal, respectively.

This program also can provide density estimates for a set of test points, specified in the "test" parameter. The density estimation tree used for this task will be the tree that was trained on the given training points, or a tree given as the parameter "input_model". The density estimates for the test points may be saved using the "test_set_estimates" output parameter.

Value

A list with several components:

output_model	Output to save trained density estimation tree to (DTree).
tag_counters_file	The file to output the number of points that went to each leaf. Default value "" (character).
tag_file	The file to output the tags (and possibly paths) for each sample in the test set. Default value "" (character).
test_set_estimates	The output estimates on the test set from the final optimally pruned tree (numeric matrix).
training_set_estimates	The output density estimates on the training set from the final optimally pruned tree (numeric matrix).
vi	The output variable importance values for each feature (numeric matrix).

Author(s)

mlpack developers

mlpack documentation built on Oct. 5, 2024, 9:08 a.m.