double_lift_chart

superglm.double_lift_chart(

y_obs,

y_pred_model,

y_pred_current,

sample_weight=None,

exposure=None,

*,

n_bins: int = 10,

labels: tuple[str, str, str] = ('Actual', 'Model', 'Current'),

ax: Axes | None = None,

) → DoubleLiftChartResult

CAS-style double lift chart (CAS RPM 2016 methodology).

Sorts by the ratio y_pred_model / y_pred_current, bins into equal-exposure quantiles, and plots three indexed series: Actual, Model, and Current — each indexed to its own overall average.

This is the standard actuarial double lift chart for comparing a new model against a current/baseline model on holdout data.

Parameters:

y_obsarray-like

Observed response values (frequency, severity, or loss ratio).

y_pred_modelarray-like

New model predictions (holdout).

y_pred_currentarray-like

Current/baseline/manual predictions (holdout).

sample_weightarray-like or None

Observation weights, read as replication mass by this comparison.

exposurearray-like or None

Exposure measure for rate models.

n_binsint

Number of equal-exposure quantile bins.

labelstuple of (str, str, str)

Display labels as (Actual, Model, Current). Each element names the corresponding series in the plot legend and axis labels.

axmatplotlib Axes or None

If provided, plot onto this axes (figure in result will be None).

Returns:

DoubleLiftChartResult

Contains a bins DataFrame and an optional figure.

References

CAS RPM 2016, “Predictive Modeling — Lift and Double Lift Charts”, https://www.casact.org/sites/default/files/presentation/rpm_2016_presentations_pm-lm-4.pdf