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Classification toolbox for matlab

version 6.0.1 (1.1 MB) by davide ballabio
The Classification toolbox for MATLAB is a collection of modules for calculating classification (supervised pattern recognition) models


Updated 26 Jan 2022

From GitHub

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Classification toolbox for MATLAB

The Classification toolbox for MATLAB is a collection of MATLAB modules for calculating classification (supervised pattern recognition) multivariate models: Discriminant Analysis, Partial Least Square Discriminant Analysis (PLSDA), Classification trees (CART), K-Nearest Neighbors (kNN), class modeling Potential Functions (Kernel Density Estimators), Support Vector Machines (SVM), Unequal class models (UNEQ), Soft Independent Modeling of Class Analogy (SIMCA), Backpropagation Neural Networks (BPNN).

This is the version 6.0 of the Classification toolbox for MATLAB

Classification toolbox for MATLAB has been released by Milano Chemometrics and QSAR research Group. Visit our website at


MATLAB should be installed, while the Statistics Toolbox is needed to compute some of the classification methods (Discriminant Analysis and CART). In order to install the toolbox, simply copy the files to a folder (e.g. "Classification toolbox for MATLAB"). Then, in order to use it, select the same folder as MATLAB current directory.


Before starting calculations, please read the HELP files provided in HTML format. A complete guide on how to calculate models is provided.


Help files are provided in HTML format. Open the help.htm file in your favourite browser and read it!


The toolbox is freeware and may be used if proper reference is given to the authors. Preferably refer to the following paper: Ballabio D, Consonni V, (2013) Classification tools in chemistry. Part 1: Linear models. PLS-DA. Analytical methods (2013), 5, 3790-3798


The Classification toolbox for MATLAB is distributed with an Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence: You are free to share - copy and redistribute the material in any medium or format. The licensor cannot revoke these freedoms as long as you follow the following license terms: Attribution - You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. NonCommercial - You may not use the material for commercial purposes. NoDerivatives - If you remix, transform, or build upon the material, you may not distribute the modified material.


Download the toolbox or check updates here: Write us for comments, questions or if you find bugs we didn't see!


Cite As

Ballabio, Davide, and Viviana Consonni. “Classification Tools in Chemistry. Part 1: Linear Models. PLS-DA.” Analytical Methods, vol. 5, no. 16, Royal Society of Chemistry (RSC), 2013, p. 3790, doi:10.1039/c3ay40582f

MATLAB Release Compatibility
Created with R2020a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.