Package

weka.classifiers.meta

Synopsis

Class for performing additive logistic regression.
This class performs classification using a regression scheme as the base learner, and can handle multi-class problems. For more information, see

J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.

Can do efficient internal cross-validation to determine appropriate number of iterations.

Options

The table below describes the options available for LogitBoost.

Option	Description
classifier	The base classifier to be used.
debug	If set to true, classifier may output additional info to the console.
likelihoodThreshold	Threshold on improvement in likelihood.
numFolds	Number of folds for internal cross-validation (default 0 means no cross-validation is performed).
numIterations	The number of iterations to be performed.
numRuns	Number of runs for internal cross-validation.
seed	The random number seed to be used.
shrinkage	Shrinkage parameter (use small value like 0.1 to reduce overfitting).
useResampling	Whether resampling is used instead of reweighting.
weightThreshold	Weight threshold for weight pruning (reduce to 90 for speeding up learning process).

Capabilities

The table below describes the capabilites of LogitBoost.

Capability	Supported
Class	Missing class values, Binary class, Nominal class
Attributes	Empty nominal attributes, Missing values, Date attributes, Nominal attributes, Numeric attributes, Binary attributes, Unary attributes
Min # of instances	1