AdaBoostM1 (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.boosting.AdaBoostM1

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Parameterized

Direct Known Subclasses:

AdaBoostM1PL
```
public class AdaBoostM1
extends Object
implements Classifier, Parameterized
```
Implementation of Experiments with a New Boosting Algorithm, by Yoav Freund&Robert E. Schapire.
This is the first AdaBoost algorithm presented in the paper, and the first boosting algorithm. Though not often mentioned, AdaBoost does support non binary classification tasks. However, for any k labels, the weak learner's error still needs to be better then 1/2, which is not an easy requirement to satisfy. For this reason, many use AdaBoostM1 by reducing k class classification problems to several 2 class problems.

Many Boosting methods, when given a binary classification task, reduce to having the same results as this class.

AdaBoost is often combined with OneVSAll to obtain better classification accuracy.

Author:

Edward Raff

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected List<Classifier>`	`hypoths` The list of weak hypothesis
`protected List<Double>`	`hypWeights` The weights for each weak learner
`protected CategoricalData`	`predicting`

Constructor Summary

Constructors
Constructor and Description

AdaBoostM1(Classifier weakLearner, int maxIterations)

Constructors
Constructor and Description
`AdaBoostM1(Classifier weakLearner, int maxIterations)`

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.
`AdaBoostM1`	`clone()`
`int`	`getMaxIterations()` Returns the maximum number of iterations used
`List<Classifier>`	`getModels()`
`List<Double>`	`getModelWeights()`
`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.
`Classifier`	`getWeakLearner()` Returns the weak learner currently being used by this method.
`void`	`setMaxIterations(int maxIterations)` Sets the maximal number of boosting iterations that may be performed
`void`	`setWeakLearner(Classifier weakLearner)` Sets the weak learner used during training.
`boolean`	`supportsWeightedData()` Indicates whether the model knows how to train using weighted data points.
`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

- Field Detail
  - hypoths
```
protected List<Classifier> hypoths
```
    The list of weak hypothesis
  - hypWeights
```
protected List<Double> hypWeights
```
    The weights for each weak learner
  - predicting
```
protected CategoricalData predicting
```
- Constructor Detail
  - AdaBoostM1
```
public AdaBoostM1(Classifier weakLearner,
                  int maxIterations)
```
- Method Detail
  - getMaxIterations
```
public int getMaxIterations()
```
    Returns the maximum number of iterations used
    
    Returns:
    
    the maximum number of iterations used
  - getModels
```
public List<Classifier> getModels()
```
    Returns:
    
    a list of the models that are in this ensemble.
  - getModelWeights
```
public List<Double> getModelWeights()
```
    Returns:
    
    a list of the models weights that are in this ensemble.
  - setMaxIterations
```
public void setMaxIterations(int maxIterations)
```
    Sets the maximal number of boosting iterations that may be performed
    
    Parameters:
    
    maxIterations - the maximum number of iterations
  - getWeakLearner
```
public Classifier getWeakLearner()
```
    Returns the weak learner currently being used by this method.
    
    Returns:
    
    the weak learner currently being used by this method.
  - setWeakLearner
```
public void setWeakLearner(Classifier weakLearner)
```
    Sets the weak learner used during training.
    
    Parameters:
    
    weakLearner - the weak learner to use
  - 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
    
    Returns:
    
    true if the model supports weighted data, false otherwise
  - clone
```
public AdaBoostM1 clone()
```
    Specified by:
    
    clone in interface Classifier
    
    Overrides:
    
    clone in class Object
  - 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 AdaBoostM1

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

hypoths

hypWeights

predicting

Constructor Detail

AdaBoostM1

Method Detail

getMaxIterations

getModels

getModelWeights

setMaxIterations

getWeakLearner

setWeakLearner

classify

trainC

trainC

supportsWeightedData

clone

getParameters

getParameter