Category: Principal Components Regression
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 …
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 …
Simulated annealing helps overcome some of the shortcomings of greedy algorithms. Here's a tutorial on simulated annealing for principal components selection in regression.
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.
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 is a staple of NIR analysis. Ridge regression is much used of machine learning. How do they relate? Find out in this post.
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.