# Category: Partial Least Squares Regression

Updated code and additional utility scripts for PLS regression. Will keep it updated as we go.

The process of developing and optimising a regression model, almost invariably requires a sequence of steps. These steps can be combined in a single predictor using the Pipeline function …

Using parallel computation to speed up cross-validation analysis for large data sets.

Backward Variable Selection for PLS regression is a method to discard variables that contribute poorly to the regression model. Here's a Python implementation of the method.

Bias-Variance trade-off refers to the optimal choice of parameters in a model in order to avoid both overfitting and underfitting. Let's look at a worked example using PLS regression.

Improve the performance of a PLS method by wavelength band selection using Simulated Annealing optimisation.

Not all wavelengths are created equals. A moving window PLS algorithm optimises the regression by discarding bands that are not useful for prediction.

Cross-validation is a standard procedure to quantify the robustness of a regression model. Compare K-Fold, Montecarlo and Bootstrap methods and learn some neat trick in the process.

Not every data point is created equal. In this post we'll show how to perform outliers detection with PLS regression for NIR spectroscopy in Python.

Improve the quality of your PLS regression using variable selection. This tutorial will work through a variable selection method for PLS in Python.

Step by step tutorial on how to build a NIR calibration model using Partial Least Squares Regression in Python.