jsat.classifiers.trees

Class DecisionTree

java.lang.Object

jsat.classifiers.trees.DecisionTree

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, TreeLearner, Parameterized, Regressor

Direct Known Subclasses:

RandomDecisionTree

public class DecisionTree
extends java.lang.Object
implements Classifier, Regressor, Parameterized, TreeLearner

Creates a decision tree from DecisionStumps. How this tree performs is controlled by pruning method selected, and the methods used in the stump.
A Decision Tree supports missing values in training and prediction.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
DecisionTree() Creates a Decision Tree that uses TreePruner.PruningMethod.REDUCED_ERROR pruning on a held out 10% of the data.
DecisionTree(int maxDepth) Creates a Decision Tree that does not do any pruning, and is built out only to the specified depth
DecisionTree(int maxDepth, int minSamples, TreePruner.PruningMethod pruningMethod, double testProportion) Creates a new decision tree classifier

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
DecisionTree	clone()
static DecisionTree	getC45Tree() Returns a Decision Tree with settings initialized so that its behavior is approximately that of the C4.5 decision tree algorithm when used on classification data.
ImpurityScore.ImpurityMeasure	getGainMethod()
int	getMaxDepth() The maximum depth that this classifier may build trees to.
int	getMinResultSplitSize() Returns the minimum result split size that may be considered for use as the attribute to split on.
int	getMinSamples() The minimum number of samples needed at each step in order to continue branching
java.util.List<Parameter>	getParameters() Returns the list of parameters that can be altered for this learner.
TreePruner.PruningMethod	getPruningMethod() Returns the method of pruning used after tree construction
double	getTestProportion() Returns the proportion of the training set that is put aside to perform pruning with
TreeNodeVisitor	getTreeNodeVisitor() Obtains a node visitor for the tree learner that can be used to traverse and predict from the learned tree
double	regress(DataPoint data)
void	setGainMethod(ImpurityScore.ImpurityMeasure gainMethod)
void	setMaxDepth(int maxDepth) Sets the maximum depth that this classifier may build trees to.
void	setMinResultSplitSize(int size) When a split is made, it may be that outliers cause the split to segregate a minority of points from the majority.
void	setMinSamples(int minSamples) Sets the minimum number of samples needed at each step in order to continue branching
void	setPruningMethod(TreePruner.PruningMethod pruningMethod) Sets the method of pruning that will be used after tree construction
void	setTestProportion(double testProportion) Sets the proportion of the training set that is put aside to perform pruning with.
boolean	supportsWeightedData() Indicates whether the model knows how to train using weighted data points.
void	train(ClassificationDataSet dataSet, boolean parallel) Trains the classifier and constructs a model for classification using the given data set.
void	train(RegressionDataSet dataSet, boolean parallel)
void	train(RegressionDataSet dataSet, java.util.Set<java.lang.Integer> options)
void	train(RegressionDataSet dataSet, java.util.Set<java.lang.Integer> options, boolean parallel)
void	trainC(ClassificationDataSet dataSet, java.util.Set<java.lang.Integer> options)

Methods inherited from class java.lang.Object

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

Methods inherited from interface jsat.classifiers.Classifier

train

Methods inherited from interface jsat.regression.Regressor

train

Methods inherited from interface jsat.parameters.Parameterized

getParameter

Constructor Detail

DecisionTree

public DecisionTree()

Creates a Decision Tree that uses TreePruner.PruningMethod.REDUCED_ERROR pruning on a held out 10% of the data.

DecisionTree

public DecisionTree(int maxDepth)

Creates a Decision Tree that does not do any pruning, and is built out only to the specified depth

Parameters:

maxDepth -

DecisionTree

public DecisionTree(int maxDepth,
                    int minSamples,
                    TreePruner.PruningMethod pruningMethod,
                    double testProportion)

Creates a new decision tree classifier

Parameters:

maxDepth - the maximum depth of the tree to create

minSamples - the minimum number of samples needed to continue branching

pruningMethod - the method of pruning to use after construction

testProportion - the proportion of the data set to put aside to use for pruning

Method Detail

regress

public double regress(DataPoint data)

Specified by:

regress in interface Regressor

train

public void train(RegressionDataSet dataSet,
                  boolean parallel)

Specified by:

train in interface Regressor

train

public void train(RegressionDataSet dataSet,
                  java.util.Set<java.lang.Integer> options)

train

public void train(RegressionDataSet dataSet,
                  java.util.Set<java.lang.Integer> options,
                  boolean parallel)

getC45Tree

public static DecisionTree getC45Tree()

Returns a Decision Tree with settings initialized so that its behavior is approximately that of the C4.5 decision tree algorithm when used on classification data. The exact behavior not identical, and certain base cases may not behave in the exact same manner. However, it uses all of the same general algorithms.

The returned tree does not perform or support

• discrete attribute grouping
• windowing
• subsidiary cutpoints (soft boundaries)

Returns:

a decision tree that will behave in a manner similar to C4.5

setGainMethod

public void setGainMethod(ImpurityScore.ImpurityMeasure gainMethod)

getGainMethod

public ImpurityScore.ImpurityMeasure getGainMethod()

setMinResultSplitSize

public void setMinResultSplitSize(int size)

When a split is made, it may be that outliers cause the split to segregate a minority of points from the majority. The min result split size parameter specifies the minimum allowable number of points to end up in one of the splits for it to be admisible for consideration.

Parameters:

size - the minimum result split size to use

getMinResultSplitSize

public int getMinResultSplitSize()

Returns the minimum result split size that may be considered for use as the attribute to split on.

Returns:

the minimum result split size in use

setMaxDepth

public void setMaxDepth(int maxDepth)

Sets the maximum depth that this classifier may build trees to.

Parameters:

maxDepth - the maximum depth of the trained tree

getMaxDepth

public int getMaxDepth()

The maximum depth that this classifier may build trees to.

Returns:

the maximum depth that this classifier may build trees to.

setMinSamples

public void setMinSamples(int minSamples)

Sets the minimum number of samples needed at each step in order to continue branching

Parameters:

minSamples - the minimum number of samples needed to branch

getMinSamples

public int getMinSamples()

The minimum number of samples needed at each step in order to continue branching

Returns:

the minimum number of samples needed at each step in order to continue branching

setPruningMethod

public void setPruningMethod(TreePruner.PruningMethod pruningMethod)

Sets the method of pruning that will be used after tree construction

Parameters:

pruningMethod - the method of pruning that will be used after tree construction

See Also:

TreePruner.PruningMethod

getPruningMethod

public TreePruner.PruningMethod getPruningMethod()

Returns the method of pruning used after tree construction

Returns:

the method of pruning used after tree construction

getTestProportion

public double getTestProportion()

Returns the proportion of the training set that is put aside to perform pruning with

Returns:

the proportion of the training set that is put aside to perform pruning with

setTestProportion

public void setTestProportion(double testProportion)

Sets the proportion of the training set that is put aside to perform pruning with.
NOTE: The values 0 and 1 are special cases.
0 indicates that no pruning will occur regardless of the set pruning method
1 indicates that the training set will be used as the testing set. This is valid for some pruning methods.

Parameters:

testProportion - the proportion, must be in the range [0, 1]

classify

public CategoricalResults classify(DataPoint data)

Description copied from interface: Classifier

Performs classification on the given data point.

Specified by:

classify in interface Classifier

Parameters:

data - the data point to classify

Returns:

the results of the classification.

train

public void train(ClassificationDataSet dataSet,
                  boolean parallel)

Description copied from interface: Classifier

Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.

Specified by:

train in interface Classifier

Parameters:

dataSet - the data set to train on

parallel - true if multiple threads should be used to train the model. false if it should be done in a single threaded manner.

trainC

public void trainC(ClassificationDataSet dataSet,
                   java.util.Set<java.lang.Integer> options)

supportsWeightedData

public boolean supportsWeightedData()

Description copied from interface: Classifier

Indicates whether the model knows how to train using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.

Specified by:

supportsWeightedData in interface Classifier

Specified by:

supportsWeightedData in interface Regressor

Returns:

true if the model supports weighted data, false otherwise

clone

public DecisionTree clone()

Specified by:

clone in interface Classifier

Specified by:

clone in interface Regressor

Overrides:

clone in class java.lang.Object

getTreeNodeVisitor

public TreeNodeVisitor getTreeNodeVisitor()

Description copied from interface: TreeLearner

Obtains a node visitor for the tree learner that can be used to traverse and predict from the learned tree

Specified by:

getTreeNodeVisitor in interface TreeLearner

Returns:

the root node visitor for the learned tree

getParameters

public java.util.List<Parameter> getParameters()

Description copied from interface: Parameterized

Returns the list of parameters that can be altered for this learner.

Specified by:

getParameters in interface Parameterized

Returns:

the list of parameters that can be altered for this learner.