## 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 …

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 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 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 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, Regression, Ridge Regression
10/19/2018

Principal components decomposition is a staple of NIR analysis. Ridge regression is much used of machine learning. How do they relate? Find out in this post

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.