575 Open source projects that are alternatives of or similar to HyperGBM

Automatic machine learning for tabular data. ⚡🔥⚡

🌳 Stacked Gradient Boosting Machines

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

Machine Learning University: Decision Trees and Ensemble Methods

Scalable machine 🤖 learning for time series forecasting.

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

AutoGluon: AutoML for Text, Image, and Tabular Data

Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning 🚀

Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.

MLBox is a powerful Automated Machine Learning python library.

[UNMAINTAINED] Automated machine learning for analytics & production

Building Decision Trees From Scratch In Python

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.

datascienv is package that helps you to setup your environment in single line of code with all dependency and it is also include pyforest that provide single line of import all required ml libraries

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

The complete code and notebooks used for the ACM Recommender Systems Challenge 2019

本人多次机器学习与大数据竞赛Top5的经验总结，满满的干货，拿好不谢

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.

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

Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries

A curated list of gradient boosting research papers with implementations.

文本分类基准测试

关注AI模型上线、模型部署

Open solution to the Santander Value Prediction Challenge 🐠

Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

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

REST web service for the true real-time scoring (<1 ms) of Scikit-Learn, R and Apache Spark models

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

AI比赛相关信息汇总

Provide an input CSV and a target field to predict, generate a model + code to run it.

Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies

Stacked Generalization (Ensemble Learning)

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Open solution to the Home Credit Default Risk challenge 🏡

Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

Comparison tools

State-of-the art Automated Machine Learning python library for Tabular Data

Python for《Deep Learning》，该书为《深度学习》(花书) 数学推导、原理剖析与源码级别代码实现

Nyoka is a Python library to export ML/DL models into PMML (PMML 4.4.1 Standard).

Performance of various open source GBM implementations

A minimal Automatic Machine Learning (AutoML) solution for tabular datasets based on PyTorch

A library for debugging/inspecting machine learning classifiers and explaining their predictions

📒 Experiment tracking tool and model registry

It's a github repo star predictor that tries to predict the stars of any github repository having greater than 100 stars.

Kaggle Kernels (Python, R, Jupyter Notebooks)

Earthquake Prediction Challenge with LightGBM and XGBoost

Automated Time Series Forecasting

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

a solution for competition of kaggle `Human or Robot`

Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training

도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.

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

Machine Learning Operator & Controller for Kubernetes

Archlinux PKGBUILDs for Data Science, Machine Learning, Deep Learning, NLP and Computer Vision

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

Bayesian Optimization and Grid Search for xgboost/lightgbm

Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

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

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

A scalable python-based framework for gene regulatory network inference using tree-based ensemble regressors.