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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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