#SHAP

Utilize SHAP values to clarify machine learning model outputs through the principles of game theory. This approach integrates optimal credit distribution with local insights across tree ensembles, deep learning models, and other algorithms. Accessible via PyPI or conda-forge, SHAP facilitates swift and precise feature attribution, beneficial for applications in NLP and ensemble models, offering deeper insights and visualization of predictive interactions.

Interpret-Community builds on the Interpret library by introducing interpretability techniques specifically for tabular data models. It offers tools for data scientists to handle real-world datasets, along with features to reverse feature engineering for original data insights and interactive visualizations. It supports various models, including PyTorch and TensorFlow, and includes explainers like SHAP and LIME. Explore examples using Jupyter notebooks to refine your machine learning model explanations.