A curated list of gradient boosting research papers with implementations.

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

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 computer vision library for ARM and x86 devices (Up to #50 times faster than OpenCV)

sciblox - Easier Data Science and Machine Learning

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

Building Decision Trees From Scratch In Python

Combining tree-boosting with Gaussian process and mixed effects models

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

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

Functional gradient boosting based on residual network perception

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

In depth machine learning resources

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.

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"

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

Rule covering for interpretation and boosting

Boosting Functional Regression Models. The current release version can be found on CRAN (http://cran.r-project.org/package=FDboost).

MILBoost and other boosting algorithms, compatible with scikit-learn

My solutions for the USC course CSCI 567: Machine Learning

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.

Machine Learning is not a MAGIC but MATH

source code of [Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs](http://proceedings.mlr.press/v108/zantedeschi20a.html)

LogitBoost classification algorithm built on top of scikit-learn

House Prices: Advanced Regression Techniques - Kaggle competition

It is a Competition for Regression Challenge held by Kaggle, It is based on a Avito Dataset whose size is 123GB which can be accessed from Kaggle, I have done Data Pre-processing, feature engineering, feature extraction, data visualization, machine learning, stacking and boosting

Implementation of Modified Online Adaboost-ing algorithm