LogitBoost (Java Statistical Analysis Tool 0.0.8 API)

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

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Parameterized

Direct Known Subclasses:

LogitBoostPL
```
public class LogitBoost
extends Object
implements Classifier, Parameterized
```
An implementation of the original 2 class LogitBoost algorithm. While there is a multi-class description in the original paper, its implementation is congruent with the result of using LogitBoost with OneVSAll classifier.
LogitBoost differs from its predecessors in that it boosts regression models to create a powerful classifier.

Paper: Special Invited Paper Additive Logistic Regression: A Statistical View of Boosting, By Jerome Friedman, Trevor Hastie and Robert Tibshirani. The Annals of Statistics 2000, Vol. 28, No. 2, 337–407

Author:

Edward Raff

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected Regressor`	`baseLearner` Weak learner to use, 'the oracle'
`protected List<Regressor>`	`baseLearners` Weak learners
`protected double`	`fScaleConstant` The constant factor that the sum of regressors is scaled by.

Constructor Summary

Constructors
Constructor and Description
`LogitBoost(int M)` Creates a new LogitBoost using the standard `MultipleLinearRegression` .
`LogitBoost(Regressor baseLearner, int M)` Creates a new LogitBoost using the given base 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.
`LogitBoost`	`clone()`
`int`	`getMaxIterations()` The maximum number of iterations of boosting that may occur.
`List<Regressor>`	`getModels()`
`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`	`getzMax()` Returns the maximum miss-classification penalty used by the algorithm.
`protected double`	`P(DataPoint x)` Returns the probability that a given data point belongs to class 1
`void`	`setMaxIterations(int maxIterations)` Sets the maximum number of iterations of boosting that can occur, giving the maximum number of base learners that may be trained
`void`	`setzMax(double zMax)` Sets the penalty bound for miss-classification of results.
`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
  - fScaleConstant
```
protected double fScaleConstant
```
    The constant factor that the sum of regressors is scaled by.
  - baseLearners
```
protected List<Regressor> baseLearners
```
    Weak learners
  - baseLearner
```
protected Regressor baseLearner
```
    Weak learner to use, 'the oracle'
- Constructor Detail
  - LogitBoost
```
public LogitBoost(int M)
```
    Creates a new LogitBoost using the standard MultipleLinearRegression .
    
    Parameters:
    
    M - the maximum number of iterations.
  - LogitBoost
```
public LogitBoost(Regressor baseLearner,
                  int M)
```
    Creates a new LogitBoost using the given base learner.
    
    Parameters:
    
    baseLearner - the weak learner to build an ensemble out of.
    
    M - the maximum number of iterations.
- Method Detail
  - getModels
```
public List<Regressor> getModels()
```
    Returns:
    
    a list of the models that are in this ensemble.
  - setMaxIterations
```
public void setMaxIterations(int maxIterations)
```
    Sets the maximum number of iterations of boosting that can occur, giving the maximum number of base learners that may be trained
    
    Parameters:
    
    maxIterations - the maximum number of iterations
  - getMaxIterations
```
public int getMaxIterations()
```
    The maximum number of iterations of boosting that may occur.
    
    Returns:
    
    maximum number of iterations of boosting that may occur.
  - setzMax
```
public void setzMax(double zMax)
```
    Sets the penalty bound for miss-classification of results. This also provides numerical stability to the algorithm. The results are not sensitive to this value. The recommended value range is in [2, 4]
    
    Parameters:
    
    zMax - the penalty bound
    
    Throws:
    
    ArithmeticException - if the value is not in (0, Double.MAX_VALUE]
  - getzMax
```
public double getzMax()
```
    Returns the maximum miss-classification penalty used by the algorithm.
    
    Returns:
    
    the maximum miss-classification
  - 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
  - P
```
protected double P(DataPoint x)
```
    Returns the probability that a given data point belongs to class 1
    
    Parameters:
    
    x - the data point in question
    
    Returns:
    
    P(y = 1 | x)
  - 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 LogitBoost 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 LogitBoost

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

fScaleConstant

baseLearners

baseLearner

Constructor Detail

LogitBoost

LogitBoost

Method Detail

getModels

setMaxIterations

getMaxIterations

setzMax

getzMax

classify

trainC

trainC

P

supportsWeightedData

clone

getParameters

getParameter