Popular GitHub Repositories for Boosting

pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks

Machine Learning University: Decision Trees and Ensemble Methods

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

Building Decision Trees From Scratch In Python

Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks [NeurIPS 2019]

sciblox - Easier Data Science and Machine Learning

Analyzing the HR Criteria of a Company and how they promote their Employees and keep Balance between them using Data Analytics, Data Visualizations, and Machine Learning Models for Classification Purposes.

Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"

Ensemble Learning for Apache Spark ?

This repository not only contains experience about parameter finetune, but also other in-practice experience such as model ensemble (boosting, bagging and stacking) in Kaggle or other competitions.

Code repository of the paper "BooVAE: Boosting Approach for Continual Learning of VAE" published at NeurIPS 2021. https://arxiv.org/abs/1908.11853

Concepts used: kNN, SVM, boosting (XGBoost, Gradient boosting, Light GBM, AdaBoost, Random Forests), deep learning (CNN, LSTM), ensembles (model stacking), transfer learning.

A boosting procedure for multitask learning on graph-structured data

Simple Demo of MLflow Project

Использование методов машинного обучения для прогнозирования инвестиций в России

AI 小项目代码、笔记

Forecast of car claim frequency using GLM, boosting and neural networks

Predicción de decisiones de un Tribunal de derechos de Consumidor usando Machine Learning

Templated boilerplate for experiments in Active and Ensemble Learning.

Exploration of Boosting Algorithms in Machine Learning: This repository hosts a seminar paper examining key boosting algorithms like AdaBoost, Gradient Boosting, and XGBoost. It includes theoretical insights, practical applications, and comparative analyses, providing a comprehensive understanding of boosting techniques in ML.