Penalised GLMs and GAM-style pricing models for insurance. SuperGLM combines
explicit feature specs, exact REML, large-n discrete REML, solver-backed
monotone splines, actuarial validation tooling, and deployable fitted
estimators for Poisson, Gamma, NB2, Tweedie, Binomial, Gaussian, and Gaussian
or Gamma location–scale models.
Install
pip install superglm
Interactive Plotly charts are optional: pip install "superglm[plotting]".
The browser model editor is included.
Fit a pricing model
from superglm import Categorical, Numeric, Spline, SuperGLM
features = {
"DrivAge": Spline(kind="ps", k=14, knot_strategy="quantile_rows"),
"VehAge": Spline(kind="cr", k=10, knot_strategy="quantile_rows"),
"BonusMalus": Spline(kind="cr", k=12, knot_strategy="quantile_tempered"),
"Area": Categorical(base="most_exposed"),
"LogDensity": Numeric(),
}
model = SuperGLM(family="poisson", features=features)
model.fit_reml(train_df, y_train, sample_weight=exposure_train)
print(model.summary())
REML chooses the smoothness of every spline. Monotone and curvature
constraints are enforced inside the fit. SuperLSS fits location, scale and
shape parameters together for severity and distributional work.
Documentation
Licence
MIT. Free for everyone, commercial use included.
1 maintainer
Metadata
1 maintainer
Release files for superglm 0.37.1
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