This repository provides a practical implementation of the Friedman and Nemenyi statistical tests specifically tailored for multi-label classification problems. These techniques are essential for evaluating and comparing the performance of multiple algorithms in experiments where each instance may belong to several classes simultaneously.
Statistical tests for detecting shared privacy concerns of bystander and visually impaired people.
Continuation to Data Analysis using more mathematical approach.
Easy statistical testing on the web
An R package implementing MARS algorithm and methods as given in Friedman's paper
writR: is an R package for automated inferential testing (for group differences) and reporting based on parametric assumptions, which are tested automatically for test selection.
An implementation of Multivariate Adaptive Regression Splines
📊 Statistical analyzers to provide more robust comparisons between Machine Learning techniques.
A set of half automated MATLAB scripts for performing analysis of thermal data (TGA, DSC) and obtaining a kinetic triplet for every reaction stage
An implementation of multivariate projection pursuit regression and univariate classification
Add a description, image, and links to the friedman topic page so that developers can more easily learn about it.
To associate your repository with the friedman topic, visit your repo's landing page and select "manage topics."