Khiops: An End-to-End, Frugal AutoML and XAI Machine Learning Solution for Large, Multi-Table Databases
Abstract
Khiops is an open source machine learning tool designed for mining large multi-table databases. Khiops is based on a unique Bayesian approach that has attracted academic interest with more than 20 publications on topics such as variable selection, classification, decision trees and co-clustering. It provides a predictive measure of variable importance using discretisation models for numerical data and value clustering for categorical data. The proposed classification/regression model is a naive Bayesian classifier incorporating variable selection and weight learning. In the case of multi-table databases, it provides propositionalisation by automatically constructing aggregates. Khiops is adapted to the analysis of large databases with millions of individuals, tens of thousands of variables and hundreds of millions of records in secondary tables. It is available on many environments, both from a Python library and via a user interface.
Cite
@article{arxiv.2508.20519,
title = {Khiops: An End-to-End, Frugal AutoML and XAI Machine Learning Solution for Large, Multi-Table Databases},
author = {Marc Boullé and Nicolas Voisine and Bruno Guerraz and Carine Hue and Felipe Olmos and Vladimir Popescu and Stéphane Gouache and Stéphane Bouget and Alexis Bondu and Luc Aurelien Gauthier and Yassine Nair Benrekia and Fabrice Clérot and Vincent Lemaire},
journal= {arXiv preprint arXiv:2508.20519},
year = {2025}
}