Boosting GitHub Repositories

A curated list of gradient boosting research papers with implementations.

Tree-Boosting, Gaussian Processes, and Mixed-Effects Models

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

Insanely fast Open Source Computer Vision library for ARM and x86 devices (Up to #50 times faster than OpenCV)

Spells for everyday living, also a book -- Models Demystified -- now available!

Python版OpenCVのTracking APIの比較サンプル

Building Decision Trees From Scratch In Python

A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and loss functions.

A collection of boosting algorithms written in Rust 🦀

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.

[OPEN teaching project] The transfer learning code for understanding and teaching : Boosting for transfer learning with single / multiple source(s)

sciblox - Easier Data Science and Machine Learning

A repository of resources for understanding the concepts of machine learning/deep learning. 

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

Error support for **idlesteam.com**

A face detection program in python using Viola-Jones algorithm.

No repository description provided.

Farm your in-game hours on Steam

In depth machine learning resources

The codes for our ACL'22 paper: PRBOOST: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning.

An implementation of "Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation" (ASONAM 2019).

This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.

Functional gradient boosting based on residual network perception

Учебные материалы по курсам связанным с Машинным обучением, которые я читаю в УрФУ. Презентации, блокноты ipynb, ссылки

We got a stew going!

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

Entire Machine Learning Hand Written Notes

Ensemble Learning for Apache Spark 🌲