CART: Fit and graph a cart model

In datascienceumh/MachineLearning: Machine Learning Algorithms for Innovation in Tourism

Description

Classification And Regression Tree is a simple technique to fit a relationship between numerical variables partitioning the target variable by a range of values of the explanatory variables. This function fits and graphs a cart model with a previous separation of training a testing datasets.

Usage

CART(formula, data, p = 0.7, nodes_min = 2, nodes_max = 18,
  includedata = FALSE, seed = NULL, ...)

Arguments

formula	a formula of the form y ~ x1 + x2 + ...
data	the data frame that contains the variables specified in formula.
p	the percentage of the training dataset to be obtained randomly.
nodes_min	Number of minimum nodes.
nodes_max	Number of maximum nodes.
includedata	logicals. If TRUE the training and testing datasets are returned.
seed	a single value, interpreted as an integer, or NULL. The default value is NULL, but for future checks of the model or models generated it is advisable to set a random seed to be able to reproduce it.
...	further arguments passed to or from other methods.

Value

A MLA object of subclass CART

Examples

## Load a Dataset
 ## Not run: 
data(EGATUR)
CART(GastoTotalD~pais+aloja+motivo,data=EGATUR)

## End(Not run)

datascienceumh/MachineLearning documentation built on June 1, 2019, 4:55 a.m.