How Decision Trees Work: From Entropy to Gini Index Explained

This article introduces decision tree algorithms, explains their role in supervised learning for classification and regression, details the construction process, compares information gain and Gini index for attribute selection, and reviews popular tree methods such as ID3, C4.5, and CART with illustrative examples.

C4.5CARTGini Index

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How Decision Trees Work: From Entropy to Gini Index Explained

Xianyu Technology

Feb 27, 2020 · Artificial Intelligence

Data-Driven Simulation for User Activity Retention Prediction

By extracting hour‑level activity logs and training supervised models—including CART, GBDT, and neural networks—on user tags, the team simulated short‑term metrics for new reward campaigns, enabling earlier prediction of next‑day retention and shortening experiment cycles despite delayed T+1 data.

AB testingCARTGBDT

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Data-Driven Simulation for User Activity Retention Prediction

AI Large-Model Wave and Transformation Guide

Oct 8, 2018 · Artificial Intelligence

Build a CART Decision Tree from Scratch in Python – Full Step‑by‑Step Guide

This article walks through a complete Python implementation of the CART decision‑tree algorithm on the Banknote dataset, covering data loading, cross‑validation splitting, Gini impurity calculation, recursive tree construction, prediction, and performance evaluation with concrete code examples.

Banknote DatasetCARTGini Index

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Build a CART Decision Tree from Scratch in Python – Full Step‑by‑Step Guide