gradient-boosting-machine

szilard/benchm-ml

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.).

benedekrozemberczki/awesome-gradient-boosting-papers

A curated list of gradient boosting research papers with implementations.

jphall663/interpretable_machine_learning_with_python

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

serengil/chefboost

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python

ledell/useR-machine-learning-tutorial

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

mdabros/SharpLearning

Machine learning for C# .Net

wepe/tgboost

Tiny Gradient Boosting Tree

szilard/GBM-perf

Performance of various open source GBM implementations

qiyiping/gbdt

sh1ng/arboretum

Gradient Boosting powered by GPU(NVIDIA CUDA)

serengil/decision-trees-for-ml

Building Decision Trees From Scratch In Python

WLOGSolutions/telco-customer-churn-in-r-and-h2o

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

RubixML/Housing

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

yubin-park/bonsai-dt

Programmable Decision Tree Framework

benedekrozemberczki/BoostedFactorization

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

WindQAQ/ML2017

NTUEE Machine Learning, 2017 Spring

haghish/mlim

mlim: single and multiple imputation with automated machine learning

rmitsuboshi/miniboosts

A collection of boosting algorithms written in Rust 🦀

RudreshVeerkhare/CustomXGBoost

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

szilard/GBM-tune

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

StatMixedML/Py-BoostLSS

An extension of Py-Boost to probabilistic modelling

joymnyaga/CreditAnalytics-Loan-Prediction

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

navdeep-G/interpretable-ml

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

serengil/h2o-ai-101

This repository covers h2o ai based implementations

gtesei/fast-furious

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

bgreenwell/MLDay18

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

xxl4tomxu98/autoencoder-feature-extraction

Use auto encoder feature extraction to facilitate classification model prediction accuracy using gradient boosting models

tugrulhkarabulut/Tree-Based-Methods

Implementation of Decision Tree and Ensemble Learning algorithms in Python with numpy

drmerlot/startml

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

leffff/stackboost

Open source gradient boosting library

RachanaJayaram/MalwarePrediction

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.

koalaverse/machine-learning-in-R

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

fahd09/kaggle_competitions_old

My contributions in Kaggle, mostly in a notebook format. Just for fun.

Mephistopheles-0/Performance-Prediction-with-Wearable-Tech-Data

analyze data from accelerometers placed on the belt, forearm, arm, and dumbbell of six participants. These individuals were tasked with executing barbell lifts, both correctly and incorrectly, in five distinct manners

perpetual-ml/perpetual

A self-generalizing, hyperparameter-free gradient boosting machine