Hitachi Vantara Pentaho Community Wiki
Child pages
  • Discretize
Skip to end of metadata
Go to start of metadata

Package

weka.filters.supervised.attribute

Synopsis

An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by Fayyad & Irani's MDL method (the default).

For more information, see:

Usama M. Fayyad, Keki B. Irani: Multi-interval discretization of continuousvalued attributes for classification learning. In: Thirteenth International Joint Conference on Articial Intelligence, 1022-1027, 1993.

Igor Kononenko: On Biases in Estimating Multi-Valued Attributes. In: 14th International Joint Conference on Articial Intelligence, 1034-1040, 1995.

Options

The table below describes the options available for Discretize.

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".

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.

useBetterEncoding

Uses a more efficient split point encoding.

useKononenko

Use Kononenko's MDL criterion. If set to false uses the Fayyad & Irani criterion.

Capabilities

The table below describes the capabilites of Discretize.

Capability

Supported

Class

Nominal class, Binary class

Attributes

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

Min # of instances

0

  • No labels