Partial Least Squares regression
GPTKB entity
Statements (47)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
statistical analysis
|
| gptkbp:advantage |
interpretation of components can be difficult
reduces dimensionality works with highly collinear data |
| gptkbp:alsoKnownAs |
gptkb:PLS_regression
|
| gptkbp:appliesTo |
gptkb:chemometrics
social sciences bioinformatics econometrics |
| gptkbp:category |
supervised learning
|
| gptkbp:developedBy |
gptkb:Herman_Wold
|
| gptkbp:extendsTo |
least squares regression
|
| gptkbp:handles |
multicollinearity
|
| https://www.w3.org/2000/01/rdf-schema#label |
Partial Least Squares regression
|
| gptkbp:implementedIn |
gptkb:Python
gptkb:SAS gptkb:MATLAB R |
| gptkbp:input |
predictor variables
response variables |
| gptkbp:introducedIn |
1960s
|
| gptkbp:output |
latent variables
regression coefficients |
| gptkbp:relatedTo |
gptkb:principal_component_regression
gptkb:factor_analysis linear regression ridge regression multivariate statistics canonical correlation analysis partial least squares path modeling |
| gptkbp:requires |
centering of data
scaling of data |
| gptkbp:supportsAlgorithm |
linear modeling
|
| gptkbp:usedFor |
gptkb:machine_learning
statistical analysis predictive modeling genomics data analysis dimension reduction multivariate calibration spectroscopy data analysis quantitative structure-activity relationship modeling |
| gptkbp:variant |
NIPALS algorithm
PLS1 PLS2 SIMPLS algorithm |
| gptkbp:bfsParent |
gptkb:PLS_regression
|
| gptkbp:bfsLayer |
8
|