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Package

weka.classifiers.meta

Synopsis

A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized. The predicted value is the expected value of the mean class value for each discretized interval (based on the predicted probabilities for each interval).

Options

The table below describes the options available for RegressionByDiscretization.

Option

Description

classifier

The base classifier to be used.

debug

If set to true, classifier may output additional info to the console.

deleteEmptyBins

Whether to delete empty bins after discretization.

numBins

Number of bins for discretization.

useEqualFrequency

If set to true, equal-frequency binning will be used instead of equal-width binning.

Capabilities

The table below describes the capabilites of RegressionByDiscretization.

Capability

Supported

Class

Date class, Missing class values, Numeric class

Attributes

Date attributes, Empty nominal attributes, Nominal attributes, Numeric attributes, Unary attributes, Binary attributes, Missing values

Min # of instances

2

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