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machine-learning
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Add CatBoost 

Add CatBoost
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Masaki Samejima 2017-11-09 23:28:07 +09:00 committed by GitHub
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* [ML-From-Scratch](https://github.com/eriklindernoren/ML-From-Scratch) - Implementations of Machine Learning models from scratch in Python with a focus on transparency. Aims to showcase the nuts and bolts of ML in an accessible way.
* [Edward](http://edwardlib.org/) - A library for probabilistic modeling, inference, and criticism. Built on top of TensorFlow.
* [xRBM](https://github.com/omimo/xRBM) - A library for Restricted Boltzmann Machine (RBM) and its conditional variants in Tensorflow.
* [CatBoost](https://github.com/catboost/catboost) - Gradient boosting on decision trees library with categorical features support out of the box for Python, R.
<a name="python-data-analysis"></a>
#### Data Analysis / Data Visualization
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* [C50](http://cran.r-project.org/web/packages/C50/index.html) - C50: C5.0 Decision Trees and Rule-Based Models.
* [caret](http://caret.r-forge.r-project.org/) - Classification and Regression Training: Unified interface to ~150 ML algorithms in R.
* [caretEnsemble](http://cran.r-project.org/web/packages/caretEnsemble/index.html) - caretEnsemble: Framework for fitting multiple caret models as well as creating ensembles of such models.
* [CatBoost](https://github.com/catboost/catboost) - Gradient boosting on decision trees library with categorical features support out of the box for Python, R.
* [Clever Algorithms For Machine Learning](https://github.com/jbrownlee/CleverAlgorithmsMachineLearning)
* [CORElearn](http://cran.r-project.org/web/packages/CORElearn/index.html) - CORElearn: Classification, regression, feature evaluation and ordinal evaluation.
* [CoxBoost](http://cran.r-project.org/web/packages/CoxBoost/index.html) - CoxBoost: Cox models by likelihood based boosting for a single survival endpoint or competing risks