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 |