1613 Open source projects that are alternatives of or similar to Lightgbm

Show how to perform fast retraining with LightGBM in different business cases

🌳 Stacked Gradient Boosting Machines

[ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples

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

Building Decision Trees From Scratch In Python

Compiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by ≥10x.

Predict people interest in renting specific NYC apartments. The challenge combines structured data, geolocalization, time data, free text and images.

A 100%-Julia implementation of Gradient-Boosting Regression Tree algorithms

MLBox is a powerful Automated Machine Learning python library.

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

An experimental Python package that reimplements AutoGBT using LightGBM and Optuna.

Simple and Distributed Machine Learning

LAMA - automatic model creation framework

머신러닝/딥러닝(PyTorch, TensorFlow) 전용 도커입니다. 한글 폰트, 한글 자연어처리 패키지(konlpy), 형태소 분석기, Timezone 등의 설정 등을 추가 하였습니다.

Browser computing unleashed!

🍊 📊 💡 Orange: Interactive data analysis

🏆 Kaggle 8th place solution

Galaxy is an asynchronous parallel visualization ray tracer for performant rendering in distributed computing environments. Galaxy builds upon Intel OSPRay and Intel Embree, including ray queueing and sending logic inspired by TACC GraviT.

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

Boosted trees in Julia

Fast and easy parallel mapreduce on HPC clusters

Examples for https://github.com/optuna/optuna

A parallel framework for population-based multi-agent reinforcement learning.

A collection of ML related stuff including notebooks, codes and a curated list of various useful resources such as books and softwares. Almost everything mentioned here is free (as speech not free food) or open-source.

Simple and Distributed Machine Learning

Machine Learning University: Decision Trees and Ensemble Methods

2017第一届腾讯社交广告高校算法大赛Rank28_code

An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

Analyze Data with Pandas-based Networks. Documentation:

a solution for competition of kaggle `Human or Robot`

A full pipeline AutoML tool for tabular data

This is a comprehensive ML techniques with python: Define the Problem- Specify Inputs & Outputs- Data Collection- Exploratory data analysis -Data Preprocessing- Model Design- Training- Evaluation

Open solution to the Mapping Challenge 🌎

Microsoft Distributed Machine Learning Toolkit

Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers.

Open solution to the Home Credit Default Risk challenge 🏡

Earthquake Prediction Challenge with LightGBM and XGBoost

A domain-specific language for parallel and distributed agent-based simulations.

A lightweight library for designing and executing workflows in .NET Core

LightGBM.jl provides a high-performance Julia interface for Microsoft's LightGBM.

Top 5th percentile solution to the Kaggle knowledge problem - Bike Sharing Demand

Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

Top 1% rankings (22/3270) code sharing for Kaggle competition Sberbank Russian Housing Market: https://www.kaggle.com/c/sberbank-russian-housing-market

Efficiently generate and analyse high dimensional data.

Library to provide Erlang style distributed computations. This library is inspired by Cloud Haskell.

Solution to Kaggle's PLAsTiCC Astronomical Classification Competition

Library for building distributed, real-time collaborative web applications

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

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

[Data Castle 算法竞赛] 精品旅行服务成单预测 final rank 11

[UNMAINTAINED] Automated machine learning for analytics & production

A curated list of data mining papers about fraud detection.

A curated list of gradient boosting research papers with implementations.

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

A hyperparameter optimization framework

Comparison tools

5th place solution for Kaggle competition Favorita Grocery Sales Forecasting

Freelook, an Electron-based client for Microsoft Outlook.

数据科学竞赛知识、代码、思路