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Interpretable Machine Learning with Python - Second Edition: Build explainable, fair, and robust high-performance models with hands-on, real-world examples

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A deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit, including SHAP, feature importance, and causal inference, to build fairer, safer, and more reliable models. Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features

  • Interpret real-world data, including cardiovascular disease data and the COMPAS recidivism scores
  • Build your interpretability toolkit with global, local, model-agnostic, and model-speci...