pyGAM
A Python library for building Generalized Additive Models (GAMs) with a scikit-learn-like API, emphasizing interpretability and performance.
What is pyGAM?
pyGAM is a Python library for building Generalized Additive Models (GAMs), which are smooth semi-parametric models that extend generalized linear models by allowing non-linear functions of features while maintaining additivity. It solves the problem of modeling complex, non-linear relationships in data while preserving model interpretability, using penalized B-splines to automatically capture these patterns.
Data scientists, statisticians, and machine learning practitioners who need interpretable models for regression and classification tasks, particularly those familiar with scikit-learn or scipy.
Developers choose pyGAM for its balance of flexibility and interpretability, offering a scikit-learn-like API that simplifies building GAMs while providing performance optimizations and the ability to incorporate prior knowledge and control overfitting.
Overview
[CONTRIBUTORS WELCOME] Generalized Additive Models in Python
Use Cases
Best For
- Building interpretable machine learning models where understanding feature effects is crucial
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