keel.Algorithms.Decision_Trees.CART

Class CART

java.lang.Object

keel.Algorithms.Decision_Trees.CART.CART

public class CART
extends java.lang.Object

Main class of algorithm CART: Classification And Regression Trees (Breiman and al., 1984) CART are binary trees

Constructor Summary

Constructors

Constructor and Description
CART(DoubleTransposedDataSet dataset) Default constructor
CART(DoubleTransposedDataSet dataset, IImpurityFunction impurityFunction) Constructor with impurity function

Method Summary

Modifier and Type	Method and Description
void	build_tree() Constructs decision tree
byte[][]	getClassificationResults(DoubleTransposedDataSet dataset) It gets the classification results
IImpurityFunction	getImpurityFunction() It returns the impurity function
int	getMaxDepth() It returns the maximal depth
double[]	getRegressionResults(DoubleTransposedDataSet dataset) It gets the regression results
DecisionTree	getTree() It returns the decision tree
boolean	isRegression() Returns if we are dealing with a regression problem
void	prune_tree() Prune decision tree
void	setImpurityFunction(IImpurityFunction impurityFunction) It sets the impurity function
void	setMaxDepth(int maxDepth) It sets the maximal depth
void	setRegression(boolean regression) It sets if we are dealing with a regression problem
boolean	stopCriteria(TreeNode node) It checks if the stop criteria has been reached

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

CART

public CART(DoubleTransposedDataSet dataset)

Default constructor

Parameters:

dataset - Dataset to learn

CART

public CART(DoubleTransposedDataSet dataset,
            IImpurityFunction impurityFunction)

Constructor with impurity function

Parameters:

dataset - Dataset to learn

impurityFunction - the impurity function

Method Detail

getTree

public DecisionTree getTree()

It returns the decision tree

Returns:

the decision tree

getImpurityFunction

public IImpurityFunction getImpurityFunction()

It returns the impurity function

Returns:

the impurityFunction

setImpurityFunction

public void setImpurityFunction(IImpurityFunction impurityFunction)

It sets the impurity function

Parameters:

impurityFunction - the impurityFunction to set

getMaxDepth

public int getMaxDepth()

It returns the maximal depth

Returns:

the maxDepth

setMaxDepth

public void setMaxDepth(int maxDepth)

It sets the maximal depth

Parameters:

maxDepth - the maxDepth to set

isRegression

public boolean isRegression()

Returns if we are dealing with a regression problem

Returns:

the regression

setRegression

public void setRegression(boolean regression)

It sets if we are dealing with a regression problem

Parameters:

regression - the regression to set

build_tree

public void build_tree()

Constructs decision tree

prune_tree

public void prune_tree()

Prune decision tree

stopCriteria

public boolean stopCriteria(TreeNode node)

It checks if the stop criteria has been reached

Parameters:

node - Node to be checked

Returns:

true if stop criteria has been reached. False otherwise

getClassificationResults

public byte[][] getClassificationResults(DoubleTransposedDataSet dataset)

It gets the classification results

Parameters:

dataset - used for checking error in

Returns:

error produced applying the data set given as argument

getRegressionResults

public double[] getRegressionResults(DoubleTransposedDataSet dataset)

It gets the regression results

Parameters:

dataset - used for checking error in

Returns:

error produced applying the data set given as argument