This repository contains demo notebooks (sample code) for the AutoMLx (automated machine learning and explainability) package from Oracle Labs.
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Updated
Oct 13, 2023
This repository contains demo notebooks (sample code) for the AutoMLx (automated machine learning and explainability) package from Oracle Labs.
A notebook exploring the paper "InterpretML: A Unified Framework for Machine Learning Interpretability" (H. Nori, S. Jenkins, P. Koch, and R. Caruana 2019).
A template for Java-based TrustyAI Jupyter notebooks
Notebook to enrich clustering going a little bit beyond Sklearn
A collection of notebooks to explore bias, fairness and explainability of machine learning models
A jupyer notebook to model explainability and inference in ML models (random forests, decision trees), using permutation importance, partial dependence plots and SHAP. Bank customer transactions dataset for classification.
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