shapash
A Python library that makes machine learning models interpretable and transparent through user-friendly visualizations and a web application.
What is shapash?
Shapash is a Python library that provides user-friendly explainability and interpretability for machine learning models. It helps data scientists and stakeholders understand how models make predictions through clear visualizations, an interactive web application, and comprehensive reporting tools. The library aims to develop reliable and transparent AI by making complex model behaviors accessible and auditable.
Data scientists, machine learning engineers, and AI auditors who need to interpret, explain, and audit machine learning models for regression, binary classification, or multiclass problems. It's also valuable for teams requiring to share model insights with non-technical stakeholders.
Developers choose Shapash for its ability to seamlessly integrate explainability into the ML workflow, offering an intuitive webapp and rich visualizations that simplify model interpretation. Its unique focus on auditability through standalone reports and quality metrics for explainability sets it apart from other interpretability tools.
Overview
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Use Cases
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