Project Name	Stars	Repos Using This	Packages Using This	Most Recent Commit	Total Releases	Latest Release	Open Issues	License	Language
Lightgbm	15,999	278	574	14 days ago	34	September 12, 2023	345	mit	C++
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.
Ml Nlp	10,874			2 years ago			29		Jupyter Notebook
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。
Synapseml	4,960		6	10 days ago	12	November 27, 2023	335	mit	Scala
Simple and Distributed Machine Learning
Mljar Supervised	2,867		2	3 months ago	84	September 26, 2023	141	mit	Python
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
Dmtk	2,745			6 years ago			1	mit
Microsoft Distributed Machine Learning Toolkit
Eli5	2,695			2 years ago	11	May 14, 2022	152	mit	Jupyter Notebook
A library for debugging/inspecting machine learning classifiers and explaining their predictions
Mars	2,664		1	4 months ago	118	June 12, 2022	212	apache-2.0	Python
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
Forecasting	2,540			a year ago				mit	Python
Time Series Forecasting Best Practices & Examples
M2cgen	2,411		3	a year ago	13	April 26, 2022	43	mit	Python
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
Awesome Decision Tree Papers	2,266			3 months ago			1	cc0-1.0	Python
A collection of research papers on decision, classification and regression trees with implementations.

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Lightgbm ⭐ 15,999

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.

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Ml Nlp ⭐ 10,874

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。

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