Getting Started#
SciKit Stan is a Python package of generalized linear models in the Stan with the familiar sk-learn interface. With scikit_stan, you can:
Compile a GLM from a highly-customizeable Stan model with control over family, link, priors, and scaling,
Perform sk-learn style model fitting with
fit()
to perform regressions based on an inference conditioned on your data. This can be done using one of Stan’s inference algorithsm:Generate posterior predictive samples from the fitted model with
predict()
,Quantify prediction quality with R-squared metric via
score()
This enables hyperparameter searching with, for example, sk-learn’s
GridSearchCV
scikit_stan wraps the CmdStanPy Python interface into Stan and provides a base for developing probabilistic models on top of Stan in Python.
This package is designed to provide a sk-learn type interface to models written in Stan.
Concretely, the sk-learn methods system
is the same here, with fit()
, predict()
and score()
methods, among others,
having the same purpose in their respective contexts.