- lime This project is about explaining what machine learning classifiers (or models) are doing.
- Shap Explain the output of any machine learning model using expectations and Shapley values.
- sklearn-expertsys Highly interpretable classifiers for scikit learn, producing easily understood decision rules instead of black box models
- ML Insights Package to understand Supervised ML Models. This package has been tested with Scikit-Learn and XGBoost library. It should work with any machine learning library that has a predict and predict_proba methods for regression and classification estimators.
- FairML an end-to-end toolbox for auditing predictive models by quantifying the relative significance of the model’s inputs.
- Edward Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields: Bayesian statistics and machine learning, deep learning, and probabilistic programming.
- SNAP: node2vec node2vec is an algorithmic framework for representational learning on graphs. Given any graph, it can learn continuous feature representations for the nodes, which can then be used for various downstream machine learning tasks.
- Probabilistic-Programming-and-Bayesian-Methods-for-Hackers An intro to Bayesian methods and probabilistic programming from a computation/understanding-first, mathematics-second point of view.
- Stan Thousands of users rely on Stan for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences, engineering, and business.
Have anything in mind that you think is awesome and would fit in this list? Feel free to send a pull request.
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