Khiops可自动处理海量多表数据，兼具建模与解释能力。
Khiops: An End-to-End, Frugal AutoML and XAI Machine Learning Solution for Large, Multi-Table Databases
- 基于贝叶斯方法自动构建聚合特征，支持多表数据融合
- 通过离散化与聚类评估变量重要性，提升模型可解释性
- 适用于百万级个体、数十万变量的大规模数据库分析
Khiops 是一款开源机器学习工具，专为挖掘大规模多表数据库设计。其基于独特的贝叶斯方法，已在变量选择、分类、决策树和共聚类等领域发表超过20篇学术论文。该工具利用离散化模型对数值数据进行处理，通过类别值聚类评估变量重要性。其分类/回归模型为引入变量选择与权重学习的朴素贝叶斯分类器。在多表数据库场景下，可自动执行命题化（propositionalisation），构建统计聚合。Khiops 适配包含数百万个体、数十万变量及数亿条辅助表记录的大型数据库，支持通过 Python 库和用户界面在多种环境中使用。
原文摘要 · Abstract (English)
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.
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