## 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 …

Regression
04/10/2021

What is the minimum amount of information required to export and re-use a linear regression model? The answer is surprisingly simple. Here's a step by step example using PLS …

Regression, Partial Least Squares Regression
03/13/2021

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.

Regression, Regression metrics, Regression Model Validation
01/09/2021

The Concordance Correlation Coefficient (CCC) can be useful to quantify the quality of a linear regression model. In this tutorial we explain the CCC and describe its relation with …

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.

Regression, Partial Least Squares Regression
08/15/2020

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

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.

Regression, Partial Least Squares Regression
12/07/2019

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.

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

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.

Partial Least Squares Regression, Regression
07/04/2018

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

Partial Least Squares Regression, Regression
06/14/2018

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