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

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

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

Building Decision Trees From Scratch In Python

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

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

Functional gradient boosting based on residual network perception

It contains online resources for understanding the critical concepts used in machine learning. 

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.

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

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.

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

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

MILBoost and other boosting algorithms, compatible with scikit-learn

In depth machine learning resources

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.

House Prices: Advanced Regression Techniques - Kaggle competition

My solutions for the USC course CSCI 567: Machine Learning

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

It is From Analytics Vidhya Hackathons, Sponsored by Club Mahindra. It is based on Regression Problem, Where Accuracy matters the most, It is measured by RMSE Score. Different Techniques such as Stacking, Ensembling, Boosting and Scientific Operations such box-cox Operations to reduce skewness of the data.

A simplified implement of Adaptive boosting.

Using Classification Techniques, Data reprocessing, Feature Engineering, Feature Extraction and Classification Algorithms from Machine Learning to Predict who can Survive the attack of Tsunami.

Solution for ENS - Societe Generale Challenge (1st place).

LogitBoost classification algorithm built on top of scikit-learn