Regression optimisation with a Pipeline
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 …
Category: Principal Components Regression
The Akaike Information Criterion for model selection
The Akaike Information Criterion (AIC) is another tool to compare prediction models. AIC combines model accuracy and parsimony in a single metric and can be used to evaluate data …
Principal component selection with simulated annealing
Principal Components Regression, Regression
02/09/2020
Simulated annealing helps overcome some of the shortcomings of greedy algorithms. Here's a tutorial on simulated annealing for principal components selection in regression.
Principal component selection with a greedy algorithm
Principal Components Regression, Regression
01/28/2020
Greedy algorithms are commonly used to optimise a function over a parameter space. Here's an implementation of a greedy algorithm for principal components selection in regression.
Principal Component Regression in Python revisited
Principal Components Regression, Regression
09/10/2019
Want to get more out of your principal components regression? Here's a simple hack that will give you a stunning improvement on the performance of PCR.
Principal Components Regression vs Ridge Regression on NIR data in Python
Principal Components Regression, Regression, Ridge Regression
10/19/2018
Principal components regression is a staple of NIR analysis. Ridge regression is much used of machine learning. How do they relate? Find out in this post.
Principal Component Regression in Python
Principal Components Regression, Regression
05/12/2018
An in-depth introduction to Principal Component Regression in Python using NIR data. PCR is the combination of PCA with linear regression. Check it out.
