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Package

weka.classifiers.meta

Synopsis

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

Options

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.

Capabilities

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

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