Partial-least squares regression
Contact: Stefan Kramer
Categories: Prediction
Exposed methods:
| predict | |
|---|---|
| Input: | |
| Output: | |
| Input format: | Weka's ARFF format |
| Output format: | Weka's ARFF format |
| User-specified parameters: | None |
| Reporting information: | Statistical measures of performance; number of components |
Description:
One way to understand Partial-least squares regression (PLS) is that it simultaneously projects the x and y
variables onto the same subspace in such a way that there is a good relationship between the predictor and
response data. Another way to see PLS is that it forms “new” x variables as linear combinations of the old ones,
and subsequently uses these new linear combinations as predictors of y.
Hence, as opposed to MLR PLS can handle correlated variables, which are noisy and possibly also incomplete.
An easy open source implementation of PLS is available in the latest WEKA release.
Background (publication date, popularity/level of familiarity, rationale of approach, further comments)
Standard statistical based method. Belongs to the family of NILES (Non-linear iterative
least squares)
Lazy learning/eager learning
Eager learning
Type of Descriptor:
Interfaces:
Priority: High
Development status:
Homepage:
Dependencies:
External components: WEKA
Technical details
Data: No
Software: Yes
Programming language(s): Java
Operating system(s): Linux, Win, Mac OS
Input format: Weka's ARFF format
Output format: Weka's ARFF format
License: GPL
