Decision Tree GitHub Repositories

:zap:机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

A collection of research papers on decision, classification and regression trees with implementations.

ID3-based implementation of the ML Decision Tree algorithm

A curated list of gradient boosting research papers with implementations.

M. Beyeler (2017). Machine Learning for OpenCV: Intelligent image processing with Python. Packt Publishing Ltd., ISBN 978-178398028-4.

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)

Connected components on discrete and continuous multilabel 3D & 2D images. Handles 26, 18, and 6 connected variants; periodic boundaries (4, 8, & 6)

Julia implementation of Decision Tree (CART) and Random Forest algorithms

A Generic Low-Code Framework Built on a Config-Driven Tree Walker

Small JavaScript implementation of ID3 Decision tree

I've demonstrated the working of the decision tree-based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample. All the steps have been explained in detail with graphics for better understanding.

numpy 实现的 周志华《机器学习》书中的算法及其他一些传统机器学习算法

A fast and easy to use decision tree learner in java

A day to day plan for this challenge. Covers both theoritical and practical aspects

Boosted trees in Julia

经典机器学习算法的极简实现

A lightweight decision making library for game AI.

Explore and compare 1K+ accurate decision trees in your browser!

Data Science algorithms and topics that you must know. (Newly Designed) Recommender Systems, Decision Trees, K-Means, LDA, RFM-Segmentation, XGBoost in Python, R, and Scala.

Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

Algorithmic trading using machine learning.

PyTorch Implementation of "Distilling a Neural Network Into a Soft Decision Tree." Nicholas Frosst, Geoffrey Hinton., 2017.

机器学习与深度学习算法示例

(ICLR 2024) GRANDE: Gradient-Based Decision Tree Ensembles

A repository for recording the machine learning code

simple rules engine

Includes top ten must know machine learning methods with R.

This repository contains Python scripts for calculating the Gini Impurity measure for each feature in a relational dataset, great for feature selection, data preprocessing, decision tree construction, binary classification tasks.