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Generalized Additive Models in Python.



pip install pygam


To speed up optimization on large models with constraints, it helps to have scikit-sparse installed because it contains a slightly faster, sparse version of Cholesky factorization. The import from scikit-sparse references nose, so you'll need that too.

The easiest way is to use Conda:
conda install -c conda-forge scikit-sparse nose

scikit-sparse docs

Contributing - HELP REQUESTED

Contributions are most welcome!

You can help pyGAM in many ways including:

  • Working on a known bug.
  • Trying it out and reporting bugs or what was difficult.
  • Helping improve the documentation.
  • Writing new distributions, and link functions.
  • If you need some ideas, please take a look at the issues.

To start:

  • fork the project and cut a new branch
  • Now install the testing dependencies
conda install pytest numpy pandas scipy pytest-cov cython
pip install --upgrade pip
pip install -r requirements.txt

It helps to add a sym-link of the forked project to your python path. To do this, you should install flit:

  • pip install flit
  • Then from main project folder (ie .../pyGAM) do: flit install -s

Make some changes and write a test...

  • Test your contribution (eg from the .../pyGAM): py.test -s
  • When you are happy with your changes, make a pull request into the master branch of the main project.


Generalized Additive Models (GAMs) are smooth semi-parametric models of the form:

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where X.T = [X_1, X_2, ..., X_p] are independent variables, y is the dependent variable, and g() is the link function that relates our predictor variables to the expected value of the dependent variable.

The feature functions f_i() are built using penalized B splines, which allow us to automatically model non-linear relationships without having to manually try out many different transformations on each variable.

GAMs extend generalized linear models by allowing non-linear functions of features while maintaining additivity. Since the model is additive, it is easy to examine the effect of each X_i on Y individually while holding all other predictors constant.

The result is a very flexible model, where it is easy to incorporate prior knowledge and control overfitting.

Citing pyGAM

Please consider citing pyGAM if it has helped you in your research or work:

Daniel Servén, & Charlie Brummitt. (2018, March 27). pyGAM: Generalized Additive Models in Python. Zenodo. DOI: 10.5281/zenodo.1208723


  author       = {Daniel Servén and
                  Charlie Brummitt},
  title        = {pyGAM: Generalized Additive Models in Python},
  month        = mar,
  year         = 2018,
  doi          = {10.5281/zenodo.1208723},
  url          = {}


  1. Simon N. Wood, 2006
    Generalized Additive Models: an introduction with R

  2. Hastie, Tibshirani, Friedman
    The Elements of Statistical Learning

  3. James, Witten, Hastie and Tibshirani
    An Introduction to Statistical Learning

  4. Paul Eilers & Brian Marx, 1996 Flexible Smoothing with B-splines and Penalties

  5. Kim Larsen, 2015
    GAM: The Predictive Modeling Silver Bullet

  6. Deva Ramanan, 2008
    UCI Machine Learning: Notes on IRLS

  7. Paul Eilers & Brian Marx, 2015
    International Biometric Society: A Crash Course on P-splines

  8. Keiding, Niels, 1991
    Age-specific incidence and prevalence: a statistical perspective

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