# dswah/pyGAM

[CONTRIBUTORS WELCOME] Generalized Additive Models in Python

Repository: https://github.com/dswah/pyGAM
Canonical: https://ross.abutalabs.com/products/pygam
Homepage: https://pygam.readthedocs.io
Language: Python
License: Apache-2.0
License Family: permissive
Topics: machine-learning, data-science, scientific-computing, python, interpretable-machine-learning, gams, explainable-ai, explainable-ml, interpretable-ai, interpretable-ml, hacktoberfest
Last push: 2026-04-21T15:12:58+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 78, release rhythm 62, longevity 100
- inputs: {"age_days": 3513, "days_push": 134, "days_rel": 258, "gap_med": 30, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1013, forks 290 (observed 2026-08-28T04:03:13.811940+00:00)

## What it is
pyGAM is a Python library for building Generalized Additive Models (GAMs) with an emphasis on modularity and performance. Its API is designed to feel familiar to users of scikit-learn and scipy.

## Use cases
- fit interpretable generalized additive models in python
- model nonlinear relationships with smooth terms
- build regression models with partial dependence per feature
- replace black-box models with explainable ml
- fit logistic gam for classification
- smooth time series or spatial data with splines

## When to choose
- you need interpretable models where each feature's effect can be visualized
- you want scikit-learn-style fit/predict API for GAMs
- you need flexible link functions and distributions like Poisson or binomial
- you want automatic smoothing parameter selection

## When to avoid
- you need deep learning or GPU-accelerated training
- you need tree ensembles like XGBoost for maximum predictive accuracy
- you work outside Python
- you need massive-scale distributed training

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, data-science, math
- domain: machine-learning, data-science
- platform: python
- tags: generalized-additive-models, gams, interpretable-ml, explainable-ai, scikit-learn-compatible, regression, statistics

## Member repositories
- dswah/pyGAM (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:13.811940+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:11:38.983020+00:00, confidence not recorded.
  - readme: https://github.com/dswah/pyGAM (fetched 2026-08-28T04:03:13.811940+00:00, sha 86297cd34a21)
- Data as of 2026-08-30T08:39:29.467469+00:00.