Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

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

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Performance of various open source GBM implementations

A self-generalizing, hyperparameter-free gradient boosting machine

HIV-1 Envelope Sequence Resistance Predictor to 33 Broadly Neutralizing Antibodies

Train Gradient Boosting models that are both high-performance *and* Fair!

Vulkan benchmark

Lightweight screen streaming server

This is the Docker container based on open source framework XGBoost (https://xgboost.readthedocs.io/en/latest/) to allow customers use their own XGBoost scripts in SageMaker.

use the data set and run the ipynb file

Detect Credit Card Fraud with Machine Learning in R

🌳 Stacked Gradient Boosting Machines

[WIP] Library used to assists in building C based applications that require vulkan renderers.

glmark2 is an OpenGL 2.0 and ES 2.0 benchmark

Project to produce supervised ML algorithm to predict which customers are likely to leave and produce .Rmd report

Sentiment140 dataset with 1.6 million tweets

Pricing and Analysis of Financial Derivative by Credit Suisse using Monte Carlo, Geometric Brownian Motion, Heston Model, CIR model, estimating greeks such as delta, gamma etc, Local volatility model incorporated with variance reduction.(For MH4518 Project)