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Package

weka.filters.unsupervised.attribute

Synopsis

Discretizes numeric attributes using equal frequency binning, where the number of bins is equal to the square root of the number of non-missing values.

For more information, see:

Ying Yang, Geoffrey I. Webb: Proportional k-Interval Discretization for Naive-Bayes Classifiers. In: 12th European Conference on Machine Learning, 564-575, 2001.

Options

The table below describes the options available for PKIDiscretize.

Option

Description

attributeIndices

Specify range of attributes to act on. This is a comma separated list of attribute indices, with "first" and "last" valid values. Specify an inclusive range with "-". E.g: "first-3,5,6-10,last".

bins

Ignored.

desiredWeightOfInstancesPerInterval

Sets the desired weight of instances per interval for equal-frequency binning.

findNumBins

Ignored.

ignoreClass

The class index will be unset temporarily before the filter is applied.

invertSelection

Set attribute selection mode. If false, only selected (numeric) attributes in the range will be discretized; if true, only non-selected attributes will be discretized.

makeBinary

Make resulting attributes binary.

useEqualFrequency

Always true.

Capabilities

The table below describes the capabilites of PKIDiscretize.

Capability

Supported

Class

Relational class, Numeric class, Binary class, No class, Empty nominal class, Missing class values, Unary class, Nominal class, String class, Date class

Attributes

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

Min # of instances

0

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