mlpack (version 4.8.0)

decision_tree_train: Decision tree training

Description

Training ID3-style decision tree model.

Usage

decision_tree_train(
  training,
  labels = NA,
  maximum_depth = 0,
  minimum_gain_split = 1e-07,
  minimum_leaf_size = 20,
  print_training_accuracy = FALSE,
  verbose = getOption("mlpack.verbose", FALSE),
  weights = NA
)

Value

A list with several components defining the class attributes:

output_model

Output for trained decision tree (DecisionTreeModel).

Arguments

training

Training dataset (may contain categorical variables) (numeric matrix/data.frame with info).

labels

Training labels (integer row).

maximum_depth

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

minimum_gain_split

Minimum gain for node splitting. Default value "1e-07" (numeric).

minimum_leaf_size

Minimum number of points in a leaf. Default value "20" (integer).

print_training_accuracy

Print the training accuracy. Default value "FALSE" (logical).

verbose

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

weights

The weight of label (numeric matrix).

Author

mlpack developers

Details

Train using a decision tree. Given a dataset containing numeric or categorical features, and associated labels for each point in the dataset, this program can train a decision tree on that data.

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 trained model is returned, and can then be used for prediction. The "minimum_leaf_size" parameter specifies the minimum number of training points that must fall into each leaf for it to be split. The "minimum_gain_split" parameter specifies the minimum gain that is needed for the node to split. The "maximum_depth" parameter specifies the maximum depth of the tree. If "print_training_accuracy" is specified, the training accuracy 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 <- decision_tree_train(training=X_train, labels=y_train,
#   minimum_leaf_size=20, minimum_gain_split=0.001)
#   }