ExtraTree (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.trees.ExtraTree

All Implemented Interfaces:

Serializable, Cloneable, Classifier, TreeLearner, Parameterized, Regressor

Direct Known Subclasses:

ERTrees
```
public class ExtraTree
extends Object
implements Classifier, Regressor, TreeLearner, Parameterized
```
The ExtraTree is an Extremely Randomized Tree. Splits are chosen at random, and the features that are selected are also chosen at random for each new node in the tree.
If set to randomly select one feature for each node, it becomes a Totally Randomized Tree
See:
Geurts, P., Ernst, D.,&Wehenkel, L. (2006). Extremely randomized trees . Machine learning, 63(1), 3–42. doi:10.1007/s10994-006-6226-1

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description
`ExtraTree()` Creates a new Extra Tree that will use all features in the training set
`ExtraTree(int selectionCount, int stopSize)` Creates a new Extra Tree

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`CategoricalResults`	`classify(DataPoint data)` Performs classification on the given data point.
`ExtraTree`	`clone()`
`ImpurityScore.ImpurityMeasure`	`getImpurityMeasure()` Returns the impurity measure in use
`Parameter`	`getParameter(String paramName)` Returns the parameter with the given name.
`List<Parameter>`	`getParameters()` Returns the list of parameters that can be altered for this learner.
`int`	`getSelectionCount()` Returns the number of random features chosen at each level in the tree
`int`	`getStopSize()` Returns the stopping size for tree growth
`TreeNodeVisitor`	`getTreeNodeVisitor()` Obtains a node visitor for the tree learner that can be used to traverse and predict from the learned tree
`boolean`	`isBinaryCategoricalSplitting()` Returns whether or not binary splitting is used for nominal features
`double`	`regress(DataPoint data)`
`void`	`setBinaryCategoricalSplitting(boolean binaryCategoricalSplitting)` The normal implementation of ExtraTree always produces binary splits, including for categorical features.
`void`	`setImpurityMeasure(ImpurityScore.ImpurityMeasure impurityMeasure)` Sets the impurity measure used during classification tree construction to select the best of the features.
`void`	`setSelectionCount(int selectionCount)` The ExtraTree will select the best of a random subset of features at each level, this sets the number of random features to select.
`void`	`setStopSize(int stopSize)` Sets the stopping size for tree growth.
`boolean`	`supportsWeightedData()` Indicates whether the model knows how to train using weighted data points.
`void`	`train(RegressionDataSet dataSet)`
`void`	`train(RegressionDataSet dataSet, ExecutorService threadPool)`
`void`	`trainC(ClassificationDataSet dataSet)` Trains the classifier and constructs a model for classification using the given data set.
`void`	`trainC(ClassificationDataSet dataSet, ExecutorService threadPool)` Trains the classifier and constructs a model for classification using the given data set.

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - ExtraTree
```
public ExtraTree()
```
    Creates a new Extra Tree that will use all features in the training set
  - ExtraTree
```
public ExtraTree(int selectionCount,
                 int stopSize)
```
    Creates a new Extra Tree
    
    Parameters:
    
    selectionCount - the number of features to select
    
    stopSize - the stop size
- Method Detail
  - setImpurityMeasure
```
public void setImpurityMeasure(ImpurityScore.ImpurityMeasure impurityMeasure)
```
    Sets the impurity measure used during classification tree construction to select the best of the features.
    
    Parameters:
    
    impurityMeasure - the impurity measure to use
  - getImpurityMeasure
```
public ImpurityScore.ImpurityMeasure getImpurityMeasure()
```
    Returns the impurity measure in use
    
    Returns:
    
    the impurity measure in use
  - setStopSize
```
public void setStopSize(int stopSize)
```
    Sets the stopping size for tree growth. When a node has less than or equal to stopSize data points to train from, it terminates and produces a leaf node.
    
    Parameters:
    
    stopSize - the size of the testing set to refuse to split
  - getStopSize
```
public int getStopSize()
```
    Returns the stopping size for tree growth
    
    Returns:
    
    the stopping size for tree growth
  - setSelectionCount
```
public void setSelectionCount(int selectionCount)
```
    The ExtraTree will select the best of a random subset of features at each level, this sets the number of random features to select. If set larger than the number of features in the training set, all features will be eligible for selection at every level.
    
    Parameters:
    
    selectionCount - the number of random features to select
  - getSelectionCount
```
public int getSelectionCount()
```
    Returns the number of random features chosen at each level in the tree
    
    Returns:
    
    the number of random features to chose
  - setBinaryCategoricalSplitting
```
public void setBinaryCategoricalSplitting(boolean binaryCategoricalSplitting)
```
    The normal implementation of ExtraTree always produces binary splits, including for categorical features. If set to false categorical features will expand out for each value in the category. This reduces the randomness of the tree.
    
    Parameters:
    
    binaryCategoricalSplitting - whether or not to use the original splitting algorithm, or to fully expand nominal features
  - isBinaryCategoricalSplitting
```
public boolean isBinaryCategoricalSplitting()
```
    Returns whether or not binary splitting is used for nominal features
    
    Returns:
    
    whether or not binary splitting is used for nominal features
  - 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.
  - trainC
```
public void trainC(ClassificationDataSet dataSet,
                   ExecutorService threadPool)
```
    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:
    
    trainC in interface Classifier
    
    Parameters:
    
    dataSet - the data set to train on
    
    threadPool - the source of threads to use.
  - trainC
```
public void trainC(ClassificationDataSet dataSet)
```
    Description copied from interface: Classifier
    
    Trains the classifier and constructs a model for classification using the given data set.
    
    Specified by:
    
    trainC in interface Classifier
    
    Parameters:
    
    dataSet - the data set to train on
  - 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 ExtraTree clone()
```
    Specified by:
    
    clone in interface Classifier
    
    Specified by:
    
    clone in interface Regressor
    
    Overrides:
    
    clone in class 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
  - regress
```
public double regress(DataPoint data)
```
    Specified by:
    
    regress in interface Regressor
  - train
```
public void train(RegressionDataSet dataSet,
                  ExecutorService threadPool)
```
    Specified by:
    
    train in interface Regressor
  - train
```
public void train(RegressionDataSet dataSet)
```
    Specified by:
    
    train in interface Regressor
  - getParameters
```
public 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.
  - getParameter
```
public Parameter getParameter(String paramName)
```
    Description copied from interface: Parameterized
    
    Returns the parameter with the given name. Two different strings may map to a single Parameter object. An ASCII only string, and a Unicode style string.
    
    Specified by:
    
    getParameter in interface Parameterized
    
    Parameters:
    
    paramName - the name of the parameter to obtain
    
    Returns:
    
    the Parameter in question, or null if no such named Parameter exists.

Class ExtraTree

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

ExtraTree

ExtraTree

Method Detail

setImpurityMeasure

getImpurityMeasure

setStopSize

getStopSize

setSelectionCount

getSelectionCount

setBinaryCategoricalSplitting

isBinaryCategoricalSplitting

classify

trainC

trainC

supportsWeightedData

clone

getTreeNodeVisitor

regress

train

train

getParameters

getParameter