## 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 Analysis
06/10/2020

Gain a practical understanding of PCA and kernel PCA by learning to code the algorithms and test it on real spectroscopic data.

Classification, Perceptron, PLS Discriminant Analysis
04/17/2020

The perceptron is a basic block of feed-forward neural networks. Learn how to use a single perceptron for binary classification of NIR spectra using gradient descent

Classification, PLS Discriminant Analysis
03/29/2020

PLS Discriminant analysis is a variation of PLS able to deal with classification problems. Here's a tutorial on binary classification with PLS-DA in Python

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.

Setting the parameters of a Savitzky-Golay filter seems more a craft than a science. Here's my method to find an optimal filter, complete with code.

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.

Meet a fairly unknown member of the spectral smoothing family: the Fourier spectral smoothing method. Learn some theory and Python code implementation.

The secret behind perfect smoothing is a wise choice of parameters. In this tutorial you will learn about the Savitzky–Golay method and the way to optimise its performance

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.

How do we make sure we are detecting only true outliers and not cherry-picking from the data? Here's a method based on the Mahalanobis distance with PCA.

Data Operations and Plotting, Plots and Charts
01/03/2019

Exploratory analysis is an essential part of data analysis. Learn a handy way to explore your dataset with NIR data correlograms with Seaborn in Python.

Classification, Linear Discriminant Analysis
12/03/2018

What is Linear Discriminant Analysis and how it differs from PCA? Let's talk trough LDA and build a NIR spectra classifier using LDA in Python.

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.

Data Operations and Plotting
08/19/2018

Three methods to export a Python NIR regression model and how to load it back for future use. Worked Python codes to discuss pros and cons of these methods.

Worked example of two scatter correction techniques for NIR spectroscopy in Python: Multiplicative Scatter Correction and Standard Normal Variate.

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.

Classification, Principal Components Analysis
03/23/2018

An in-depth tutorial on how to run a classification of NIR spectra using Principal Component Analysis in Python. Step by step example with code.

Classification, Principal Components Analysis
07/06/2017

Can we use NIR analysis to grade macadamias? Check out our preliminary results of NIR classification of macadamia kernels using Principal Component Analysis.

Classification, Principal Components Analysis
03/21/2017

A worked example for an introduction to Principal Component Analysis in Python.