ERTrees (Java Statistical Analysis Tool 0.0.8 API)

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

All Implemented Interfaces:

Serializable, Cloneable, Classifier, TreeLearner, Parameterized, Regressor
```
public class ERTrees
extends ExtraTree
```
Extra Randomized Trees (ERTrees) is an ensemble method built on top of ExtraTree. The randomness of the trees provides incredibly high variance, yet a low bias. The sum of many randomized trees proves to be a powerful and fast learner.
The default settings are those suggested in the paper. However, the default stop size suggested (especially for classification) is often too small. You may want to consider increasing it if the accuracy is too low.
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

ERTrees()
Creates a new Extremely Randomized Trees learner

ERTrees(int forrestSize)
Creates a new Extremely Randomized Trees learner

Constructors
Constructor and Description
`ERTrees()` Creates a new Extremely Randomized Trees learner
`ERTrees(int forrestSize)` Creates a new Extremely Randomized Trees learner

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.
`ERTrees`	`clone()`
`<Type extends DataSet> OnLineStatistics[]`	`evaluateFeatureImportance(DataSet<Type> data)` Measures the statistics of feature importance from the trees in this forest.
`<Type extends DataSet> OnLineStatistics[]`	`evaluateFeatureImportance(DataSet<Type> data, TreeFeatureImportanceInference imp)` Measures the statistics of feature importance from the trees in this forest.
`int`	`getForrestSize()`
`TreeNodeVisitor`	`getTreeNodeVisitor()` Obtains a node visitor for the tree learner that can be used to traverse and predict from the learned tree
`boolean`	`getUseDefaultSelectionCount()` Returns if the default heuristic for the selection count is used
`boolean`	`getUseDefaultStopSize()` Returns if the default heuristic for the stop size is used
`double`	`regress(DataPoint data)`
`void`	`setForrestSize(int forrestSize)`
`void`	`setUseDefaultSelectionCount(boolean useDefaultSelectionCount)` Sets whether or not to use the default heuristic for the number of random features to select as candidates for each node.
`void`	`setUseDefaultStopSize(boolean useDefaultStopSize)` Sets whether or not to us the default heuristic for the number of points to force a new node to be a leaf.
`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 jsat.classifiers.trees.ExtraTree
getImpurityMeasure, getParameter, getParameters, getSelectionCount, getStopSize, isBinaryCategoricalSplitting, setBinaryCategoricalSplitting, setImpurityMeasure, setSelectionCount, setStopSize

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

- Constructor Detail
  - ERTrees
```
public ERTrees()
```
    Creates a new Extremely Randomized Trees learner
  - ERTrees
```
public ERTrees(int forrestSize)
```
    Creates a new Extremely Randomized Trees learner
    
    Parameters:
    
    forrestSize - the number of trees to construct
- Method Detail
  - evaluateFeatureImportance
```
public <Type extends DataSet> OnLineStatistics[] evaluateFeatureImportance(DataSet<Type> data)
```
    Measures the statistics of feature importance from the trees in this forest. For classification datasets, the MDI method with Gini impurity will be used. For others, the ImportanceByUses method will be used. This may change in the future.
    
    Type Parameters:
    
    Type -
    
    Parameters:
    
    data - the dataset to infer the feature importance from with respect to the current model.
    
    Returns:
    
    an array of statistics, which each index corresponds to a specific feature. Numeric features start from the zero index, categorical features start from the index equal to the number of numeric features.
  - evaluateFeatureImportance
```
public <Type extends DataSet> OnLineStatistics[] evaluateFeatureImportance(DataSet<Type> data,
                                                                           TreeFeatureImportanceInference imp)
```
    Measures the statistics of feature importance from the trees in this forest.
    
    Type Parameters:
    
    Type -
    
    Parameters:
    
    data - the dataset to infer the feature importance from with respect to the current model.
    
    imp - the method of determing the feature importance that will be applied to each tree in this model
    
    Returns:
    
    an array of statistics, which each index corresponds to a specific feature. Numeric features start from the zero index, categorical features start from the index equal to the number of numeric features.
  - setUseDefaultSelectionCount
```
public void setUseDefaultSelectionCount(boolean useDefaultSelectionCount)
```
    Sets whether or not to use the default heuristic for the number of random features to select as candidates for each node. If true the value of selectionCount will be modified during training, using sqrt(n) features for classification and all features for regression. Otherwise, whatever value set before hand will be used.
    
    Parameters:
    
    useDefaultSelectionCount - whether or not to use the heuristic version
  - getUseDefaultSelectionCount
```
public boolean getUseDefaultSelectionCount()
```
    Returns if the default heuristic for the selection count is used
    
    Returns:
    
    if the default heuristic for the selection count is used
  - setUseDefaultStopSize
```
public void setUseDefaultStopSize(boolean useDefaultStopSize)
```
    Sets whether or not to us the default heuristic for the number of points to force a new node to be a leaf. If true the value for stopSize will be altered during training, set to 2 for classification and 5 for regression. Otherwise, whatever value set beforehand will be used.
    
    Parameters:
    
    useDefaultStopSize - whether or not to use the heuristic version
  - getUseDefaultStopSize
```
public boolean getUseDefaultStopSize()
```
    Returns if the default heuristic for the stop size is used
    
    Returns:
    
    if the default heuristic for the stop size is used
  - setForrestSize
```
public void setForrestSize(int forrestSize)
```
  - getForrestSize
```
public int getForrestSize()
```
  - classify
```
public CategoricalResults classify(DataPoint data)
```
    Description copied from interface: Classifier
    
    Performs classification on the given data point.
    
    Specified by:
    
    classify in interface Classifier
    
    Overrides:
    
    classify in class ExtraTree
    
    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
    
    Overrides:
    
    trainC in class ExtraTree
    
    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
    
    Overrides:
    
    trainC in class ExtraTree
    
    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
    
    Overrides:
    
    supportsWeightedData in class ExtraTree
    
    Returns:
    
    true if the model supports weighted data, false otherwise
  - regress
```
public double regress(DataPoint data)
```
    Specified by:
    
    regress in interface Regressor
    
    Overrides:
    
    regress in class ExtraTree
  - train
```
public void train(RegressionDataSet dataSet,
                  ExecutorService threadPool)
```
    Specified by:
    
    train in interface Regressor
    
    Overrides:
    
    train in class ExtraTree
  - train
```
public void train(RegressionDataSet dataSet)
```
    Specified by:
    
    train in interface Regressor
    
    Overrides:
    
    train in class ExtraTree
  - clone
```
public ERTrees clone()
```
    Specified by:
    
    clone in interface Classifier
    
    Specified by:
    
    clone in interface Regressor
    
    Overrides:
    
    clone in class ExtraTree
  - 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
    
    Overrides:
    
    getTreeNodeVisitor in class ExtraTree
    
    Returns:
    
    the root node visitor for the learned tree

Class ERTrees

Constructor Summary

Method Summary

Methods inherited from class jsat.classifiers.trees.ExtraTree

Methods inherited from class java.lang.Object

Constructor Detail

ERTrees

ERTrees

Method Detail

evaluateFeatureImportance

evaluateFeatureImportance

setUseDefaultSelectionCount

getUseDefaultSelectionCount

setUseDefaultStopSize

getUseDefaultStopSize

setForrestSize

getForrestSize

classify

trainC

trainC

supportsWeightedData

regress

train

train

clone

getTreeNodeVisitor