RacedIncrementalLogitBoost

Package

weka.classifiers.meta

Classifier for incremental learning of large datasets by way of racing logit-boosted committees.

The table below describes the options available for RacedIncrementalLogitBoost.

Option	Description
classifier	The base classifier to be used.
debug	If set to true, classifier may output additional info to the console.
maxChunkSize	The maximum number of instances to train the base learner with. The chunk sizes used will start at minChunkSize and grow twice as large for as many times as they are less than or equal to the maximum size.
minChunkSize	The minimum number of instances to train the base learner with.
pruningType	The pruning method to use within each committee. Log likelihood pruning will discard new models if they have a negative effect on the log likelihood of the validation data.
seed	The random number seed to be used.
useResampling	Force the use of resampling data rather than using the weight-handling capabilities of the base classifier. Resampling is always used if the base classifier cannot handle weighted instances.
validationChunkSize	The number of instances to hold out for validation. These instances will be taken from the beginning of the stream, so learning will not start until these instances have been consumed first.

The table below describes the capabilites of RacedIncrementalLogitBoost.

Capability	Supported
Class	Binary class, Missing class values, Nominal class
Attributes	Missing values, Empty nominal attributes, Binary attributes, Nominal attributes, Numeric attributes, Date attributes, Unary attributes
Min # of instances	0