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plot oblique decision tree depth

Source: R/plot_ODT_depth.R
plot_ODT_depth.Rd

Draw the error graph of class ODT at different depths.

Usage

plot_ODT_depth(
  formula,
  data = NULL,
  newdata = NULL,
  split = "gini",
  NodeRotateFun = "RotMatPPO",
  paramList = NULL,
  digits = NULL,
  main = NULL,
  ...
)

Arguments

formula

Object of class formula with a response describing the model to fit. If this is a data frame, it is taken as the model frame. (see model.frame)

data

Training data of class data.frame in ODT used to calculate the OOB error.

newdata

A data frame or matrix containing new data is used to calculate the test error. If it is missing, then it is replaced by data.

split

The criterion used for splitting the variable. 'gini': gini impurity index (classification, default), 'entropy': information gain (classification) or 'mse': mean square error (regression).

NodeRotateFun

Name of the function of class character that implements a linear combination of predictors in the split node. including

  • "RotMatPPO": projection pursuit optimization model (PPO), see RotMatPPO (default, model="PPR").

  • "RotMatRF": single feature similar to Random Forest, see RotMatRF.

  • "RotMatRand": random rotation, see RotMatRand.

  • "RotMatMake": Users can define this function, for details see RotMatMake.

paramList

List of parameters used by the functions NodeRotateFun. If left unchanged, default values will be used, for details see defaults.

digits

Integer indicating the number of decimal places (round) or significant digits (signif) to be used.

main

main title

...

Arguments to be passed to methods.

Value

OOB error and test error of newdata, misclassification rate (MR) for classification or mean square error (MSE) for regression.

See also

ODT plot.ODT

Examples

data(body_fat)
set.seed(221212)
train <- sample(1:252, 100)
train_data <- data.frame(body_fat[train, ])
test_data <- data.frame(body_fat[-train, ])
plot_ODT_depth(Density ~ ., train_data, test_data, split = "mse")