mlpack (version 4.8.0)

random_forest_train: Random Forests train

Description

An implementation of the standard random forest algorithm by Leo Breiman for classification. Given labeled data, a random forest is trained.

Usage

random_forest_train(
  labels,
  training,
  maximum_depth = 0,
  minimum_gain_split = 0,
  minimum_leaf_size = 1,
  num_trees = 10,
  print_training_accuracy = FALSE,
  seed = 0,
  subspace_dim = 0,
  verbose = getOption("mlpack.verbose", FALSE)
)

Value

A list with several components defining the class attributes:

output_model

Model to save trained random forest to (RandomForestModel).

Arguments

labels

Labels for training dataset (integer row).

training

Training dataset (numeric matrix).

maximum_depth

Maximum depth of the tree (0 means no limit). Default value "0" (integer).

minimum_gain_split

Minimum gain needed to make a split when building a tree. Default value "0" (numeric).

minimum_leaf_size

Minimum number of points in each leaf node. Default value "1" (integer).

num_trees

Number of trees in the random forest. Default value "10" (integer).

print_training_accuracy

If set, then the accuracy of the model on the training set will be predicted (verbose must also be specified). Default value "FALSE" (logical).

seed

Random seed. If 0, 'std::time(NULL)' is used. Default value "0" (integer).

subspace_dim

Dimensionality of random subspace to use for each split. '0' will autoselect the square root of data dimensionality. Default value "0" (integer).

verbose

Display informational messages and the full list of parameters and timers at the end of execution. Default value "getOption("mlpack.verbose", FALSE)" (logical).

Author

mlpack developers

Details

This program is an implementation of the standard random forest classification algorithm by Leo Breiman. A random forest is trained (and returned for later use for subsequent use where predictions or class probabilities for points may be generated.

The training set and associated labels are specified with the "training" and "labels" parameters, respectively. The labels should be in the range `[0, num_classes - 1]`. Optionally, if "labels" is not specified, the labels are assumed to be the last dimension of the training dataset.

The "minimum_leaf_size" parameter specifies the minimum number of training points that must fall into each leaf for it to be split. The "num_trees" controls the number of trees in the random forest. The "minimum_gain_split" parameter controls the minimum required gain for a decision tree node to split. Larger values will force higher-confidence splits. The "maximum_depth" parameter specifies the maximum depth of the tree. The "subspace_dim" parameter is used to control the number of random dimensions chosen for an individual node's split. If "print_training_accuracy" is specified, the calculated accuracy on the training set will be printed.

Examples

# 
# #' # \dontrun{
# suppressMessages(library(mlpack)) # in case 'mlpack' is not yet loaded
# X <- as.matrix(read.csv("http://datasets.mlpack.org/iris.csv",
# header=FALSE))
# y <- as.matrix(read.csv("http://datasets.mlpack.org/iris_labels.csv",
# header=FALSE))
# pp <- preprocess_split(input=X, input_label=as.matrix(1:nrow(X)),
# test_ratio=0.2)
# X_train <- pp[["training"]]
# X_test <- pp[["test"]]
# # labels are indices to operate on both factors or numeric data
# y_train <- y[as.integer(pp[["training_labels"]]), 1]
# y_test <- y[as.integer(pp[["test_labels"]]), 1]
# 
# model <- random_forest_train(training=X_train, labels=y_train,
#   minimum_leaf_size=20, num_trees=10, print_training_accuracy=TRUE)
#   }