| EpistasisLab/tpot |
9,385 |
|
40 |
22 |
over 2 years ago |
62 |
August 15, 2023 |
284 |
lgpl-3.0 |
Python |
| A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. |
| h2oai/h2o-3 |
7,487 |
|
62 |
33 |
3 days ago |
49 |
August 09, 2023 |
2,746 |
apache-2.0 |
Jupyter Notebook |
| 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. |
| biolab/orange3 |
4,469 |
|
57 |
47 |
about 2 years ago |
63 |
October 31, 2023 |
99 |
other |
Python |
| 🍊 :bar_chart: :bulb: Orange: Interactive data analysis |
| microsoft/FLAML |
3,500 |
|
0 |
11 |
about 2 years ago |
92 |
October 02, 2023 |
210 |
mit |
Jupyter Notebook |
| A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP. |
| mljar/mljar-supervised |
2,867 |
|
0 |
2 |
about 2 years 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 |
| parrt/dtreeviz |
2,720 |
|
2 |
22 |
over 2 years ago |
41 |
July 07, 2022 |
61 |
mit |
Jupyter Notebook |
| A python library for decision tree visualization and model interpretation. |
| tirthajyoti/Machine-Learning-with-Python |
2,712 |
|
0 |
0 |
almost 3 years ago |
0 |
|
8 |
bsd-2-clause |
Jupyter Notebook |
| Practice and tutorial-style notebooks covering wide variety of machine learning techniques |
| benedekrozemberczki/awesome-decision-tree-papers |
2,266 |
|
0 |
0 |
about 2 years ago |
0 |
|
1 |
cc0-1.0 |
Python |
| A collection of research papers on decision, classification and regression trees with implementations. |
| szilard/benchm-ml |
1,839 |
|
0 |
0 |
over 3 years ago |
0 |
|
11 |
mit |
R |
| 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.). |
| kingfengji/gcForest |
1,242 |
|
0 |
0 |
about 5 years ago |
0 |
|
35 |
|
Python |
| This is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks' |
