Multi-class classification aims and subdividing samples into one of multiple predefined categories. In this post we explore a basic classifier and discuss important metrics such as accuracy and AUC …
The PCA correlation circle is a useful tool to visually display the correlation between spectral bands and principal components. The correlation can be quantified through the Euclidean distance and …
Gain a practical understanding of PCA and kernel PCA by learning to code the algorithms and test it on real spectroscopic data.
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
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
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.
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.
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.
Can we use NIR analysis to grade macadamias? Check out our preliminary results of NIR classification of macadamia kernels using Principal Component Analysis.
A worked example for an introduction to Principal Component Analysis in Python.