PLSFilter
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 |