A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

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

A curated list of gradient boosting research papers with implementations.

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

useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html

Tiny Gradient Boosting Tree

Machine learning for C# .Net

Performance of various open source GBM implementations

Lightweight Decision Trees Framework supporting Gradient Boosting (GBDT, GBRT, GBM), Random Forest and Adaboost w/categorical features support for Python

Gradient Boosting powered by GPU(NVIDIA CUDA)

Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)

Building Decision Trees From Scratch In Python

NTUEE Machine Learning, 2017 Spring

Programmable Decision Tree Framework

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

An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine.

Tuning GBMs (hyperparameter tuning) and impact on out-of-sample predictions

code (R, Matlab/Octave), models and meta-models I needed in my Machine Learning Lab but I didn't found on the shelf

Material from "Random Forests and Gradient Boosting Machines in R" presented at Machine Learning Day '18

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

This repository covers h2o ai based implementations

XGBoost implementation from scratch with Numpy using Adam and RSMProp optimizers.

A predictive model that uses several machine learning algorithms to predict the eligibility of loan applicants based on several factors

R package for automatic hyper parameter tuning and ensembles with deep learning, gradient boosting machines, and random forests. Powered by h2o.

All the Workings for GSOC-2019

A bookdown version of the UseR 2016 machine learning tutorial given by Erin LeDell

How to build classification models using H2O in R

Using machine learning models to predict the probability of a windows system getting infected by various families of malware, based on different properties of that system.

One Data Set with All Algorithms