Inspecting Results¶

Summary table¶

Statsmodels-style summary with SEs, p-values, and smooth tests:

m = model.metrics(df, y, sample_weight=exposure)
print(m.summary())

Per-term inference¶

The TermInference dataclass holds everything about a single term: grid values, relativities, confidence intervals, spline metadata.

ti = model.term_inference("DrivAge")

ti.x                        # evaluation grid (spline/polynomial) or levels (categorical)
ti.relativity               # exp(f(x)) relativity curve
ti.ci_lower, ti.ci_upper    # pointwise CI bounds
ti.edf                      # effective degrees of freedom
ti.spline                   # SplineMetadata (interior_knots, boundary_knots, basis_dim, ...)

For a spline-backed OrderedCategorical, inference is a single whole-smooth Wood test. Its ordered level rows report effect estimates, standard errors, and confidence intervals for interpretation; they intentionally do not present separate level p-values or significance stars.

By default this is the canonical fitted term contribution under the model's identifiability constraint. If you want a rebased reporting view where the geometric mean of relativities is 1, pass centering="mean" explicitly:

ti = model.term_inference("DrivAge", centering="mean")

Plotting¶

All plotting goes through model.plot():

# Single-term chart
model.plot("DrivAge", X=df, sample_weight=exposure)

# All terms in a grid
model.plot(X=df, sample_weight=exposure)

# Subset of terms
model.plot(["DrivAge", "VehAge"], X=df, sample_weight=exposure)

# Interactive Plotly main-effect explorer
model.plot(engine="plotly", X=df, sample_weight=exposure)

# Plotly subset explorer
model.plot(["DrivAge", "VehAge"], engine="plotly", X=df, sample_weight=exposure)

# Plotly interaction contour + exposure HDR view
model.plot(
    "DrivAge:VehAge",
    engine="plotly",
    interaction_view="contour_pair",
    X=df,
    sample_weight=exposure,
)

# Interaction
model.plot("DrivAge:Area")

engine="matplotlib" is the chart/export path. engine="plotly" is the multi-term main-effect explorer path and requires at least two main effects (or terms=None).

Options: ci ("pointwise", "simultaneous", "both", None, False), show_knots, show_density, title, subtitle, engine.

Plot data export¶

Use model.plot_data() when you need the underlying x/y/grid data to rebuild a plot outside SuperGLM:

# Main-effect data
payload = model.plot_data("DrivAge", X=df, sample_weight=exposure, show_knots=True)
curve_df = payload["terms"][0]["effect"]
density_df = payload["terms"][0]["density"]
knots_df = payload["terms"][0]["knots"]

# Continuous x continuous interaction grid
payload = model.plot_data("DrivAge:VehAge", X=df, sample_weight=exposure, n_points=220)
surface_df = payload["effect"]
hdr_df = payload["density"]  # includes density + hdr_mass columns

Relativity DataFrames¶

For manual access or export:

rels = model.relativities(with_se=True)  # canonical fitted-term view
# dict of {feature_name: DataFrame}

Use centering="mean" only when you explicitly want to rebase each term for reporting or cross-feature comparison:

rels = model.relativities(with_se=True, centering="mean")

Rating table export¶

Use export_rating_tables() to create deployment-oriented Excel tables with binned spline effects:

model.export_rating_tables(
    "rating_tables.xlsx",
    X_train,
    y_train,
    sample_weight=exposure_train,
    n_bins=150,
)

The workbook includes selected-bin rating tables, a discretization impact sweep for 20, 50, 100, 200, 250 bins, and a structured Model Summary sheet. The ModelOverview and TermInference Excel tables use typed cells for metrics, estimates, intervals, and p-values instead of storing the formatted console summary as one text column. The editor exposes the same workbook through Export > Excel rating workbook when explicit training or retained fit data is available.

Families¶

Family	Variance function	Use case
`Poisson()`	V(μ) = μ	Claim frequency
`NegativeBinomial(theta=1.0)`	V(μ) = μ + μ²/θ	Overdispersed frequency
`Gamma()`	V(μ) = μ²	Claim severity
`Tweedie(p=1.5)`	V(μ) = μᵖ	Pure premium (frequency × severity)