ModestAdaBoost (Java Statistical Analysis Tool 0.0.8 API)

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

All Implemented Interfaces:

Serializable, Cloneable, BinaryScoreClassifier, Classifier, Parameterized
```
public class ModestAdaBoost
extends Object
implements Classifier, Parameterized, BinaryScoreClassifier
```
Modest Ada Boost is a generalization of Discrete Ada Boost that attempts to reduce the generalization error and avoid over-fitting. Empirically, ModestBoost usually maintains a higher training-set error, and may take more iterations to obtain the same test set error as other algorithms, but doesn't not increase as much after it reaches the minimum error - which should make it easier to obtain the higher accuracy.
See:
Vezhnevets, A.,&Vezhnevets, V. (2005). “Modest AdaBoost” – Teaching AdaBoost to Generalize Better. GraphiCon. Novosibirsk Akademgorodok, Russia. Retrieved from here

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
Modifier	Constructor and Description
	`ModestAdaBoost(Classifier weakLearner, int maxIterations)` Creates a new ModestBoost learner
`protected`	`ModestAdaBoost(ModestAdaBoost toClone)` Copy constructor

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.
`ModestAdaBoost`	`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.
`double`	`getScore(DataPoint dp)` Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.
`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
  - ModestAdaBoost
```
public ModestAdaBoost(Classifier weakLearner,
                      int maxIterations)
```
    Creates a new ModestBoost learner
    
    Parameters:
    
    weakLearner - the weak learner to use
    
    maxIterations - the maximum number of boosting iterations
  - ModestAdaBoost
```
protected ModestAdaBoost(ModestAdaBoost toClone)
```
    Copy constructor
    
    Parameters:
    
    toClone - the object to clone
- Method Detail
  - 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.
  - getMaxIterations
```
public int getMaxIterations()
```
    Returns the maximum number of iterations used
    
    Returns:
    
    the maximum number of iterations used
  - 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
  - getScore
```
public double getScore(DataPoint dp)
```
    Description copied from interface: BinaryScoreClassifier
    
    Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.
    
    Specified by:
    
    getScore in interface BinaryScoreClassifier
    
    Parameters:
    
    dp - the data point to predict the class label of
    
    Returns:
    
    the score for the given data point
  - 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 ModestAdaBoost clone()
```
    Specified by:
    
    clone in interface BinaryScoreClassifier
    
    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 ModestAdaBoost

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

hypoths

hypWeights

predicting

Constructor Detail

ModestAdaBoost

ModestAdaBoost

Method Detail

getModels

getModelWeights

getMaxIterations

setMaxIterations

getWeakLearner

setWeakLearner

getScore

classify

trainC

trainC

supportsWeightedData

clone

getParameters

getParameter