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Package

weka.filters.supervised.attribute

Synopsis

Runs Partial Least Square Regression over the given instances and computes the resulting beta matrix for prediction.
By default it replaces missing values and centers the data.

For more information see:

Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002). A User Friendly Guide to Multivariate Calibration and Classification. NIR Publications.

StatSoft, Inc.. Partial Least Squares (PLS).

Bent Jorgensen, Yuri Goegebeur. Module 7: Partial least squares regression I.

S. de Jong (1993). SIMPLS: an alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems. 18:251-263.

Options

The table below describes the options available for PLSFilter.

Option

Description

algorithm

Sets the type of algorithm to use.

debug

Turns on output of debugging information.

numComponents

The number of components to compute.

performPrediction

Whether to update the class attribute with the predicted value.

preprocessing

Sets the type of preprocessing to use.

replaceMissing

Whether to replace missing values.

Capabilities

The table below describes the capabilites of PLSFilter.

Capability

Supported

Class

Date class, Numeric class

Attributes

Missing values, Date attributes, Numeric attributes

Min # of instances

0

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