PLSLOGIT

Overview

PLSLOGIT combines PLS Regression and Logistic Regression. It can handle multiclass problem i.e. the target variable can have \(K \left(K \geq 2\right)\) classes. It relies on the same principle PLSLDA about the number of components detection.

Description of the method

There are two main steps in the learning process. Firstly, using the same coding scheme as PLSDA, we launch the PLS algorithm. Secondly, we launch the logistic regression on the X component scores (factors). This kind of data transformation is very useful when the original input variables are highly correlated.

Predictive idea

The classification rule is the same as the logistic regression component.

Number of components

In PLSLOGIT procedure, we can explicitly specify the number of components, with the parameter n_components, for NIPALS [1] algorithms.

References

[1]

NonLinear Iterative Partial Least Squares.