Regression

Premium pricing, claims severity, claims frequency — anything with a continuous target.

context = StructuredGateContext(
    model=pricing_model,
    X=X_val,
    y_true=realised_loss,
    y_pred=quoted_premium,
    protected_df=protected_val,
    expected_loss=technical_premium,  # enables loss-ratio parity
    task="regression",
)

config = GateConfig()
config.performance.metric = "rmse"
config.performance.max_error = 45_000.0

Metrics point in two directions

This is the change that catches people out. Error metrics are lower-is-better, so they are gated with max_error, not min_score.

Metric	Direction	Gated with	Use for
r2	higher better	min_score	scale-free; the "auto" default
rmse	lower better	max_error	premium, general
mae	lower better	max_error	robust to outliers
mape	lower better	max_error	skewed money — claims severity
poisson_deviance	lower better	max_error	counts — claims frequency

All five are implemented in numpy, so they work on a core install without scikit-learn.

There is deliberately no default max_error — a sensible ceiling depends entirely on whether the target is naira or claim counts — so configuring an error metric without one raises GateConfigurationError rather than passing silently. The comparison is the entire point of the gate.

Choosing for the target's shape

• Severity is money and heavily right-skewed. A handful of large claims dominate squared error, so rmse measures the tail rather than typical accuracy. mape scores relative error, which is usually what a reserving team cares about — but it is undefined where the actual is zero.
• Frequency is a count, mostly zero. Squared error understates over-dispersion and treats "predicted 0.1 claims, saw 3" far too gently. poisson_deviance is built for it, and needs strictly positive predictions since it takes their log.

Fairness without a selected class

Demographic parity has no regression analogue. Four checks replace it, each answering something the others cannot.

Check	Question	Needs
loss_ratio_parity	higher margin over own expected loss?	expected_loss
group_mean_gap	systematically higher predictions?	—
error_parity	model materially less accurate for a group?	y_true
calibration_parity	predictions over- or under-shoot reality?	y_true

All gaps are measured relative to the overall figure, so one threshold works whether the target is naira or claim counts. Groups smaller than FairnessConfig.min_group_size (default 30) are reported but not scored — a three-policy segment otherwise produces a wild ratio that reads as a finding.

Why loss-ratio parity is the one that matters

A pricing model should charge more in a higher-loss segment. That is risk-based pricing, not discrimination, and a check that flags it makes the gate noisy enough to ignore.

LossRatioParityCheck divides each group's premium by its own expected cost, which isolates unfairness from actuarially justified variation:

region: premium-to-expected-loss ratio spans 1.123 (Lagos) to 1.412 (Kano)
        — 23.6% of the overall ratio 1.225; Kano carries the higher margin
        over its own expected cost

Same risk, different margin. group_mean_gap alone would only have told you Kano pays more, which is ambiguous.

It requires context.expected_loss — a per-row expected loss, technical premium or pure premium. Without it the check reports NOT_APPLICABLE rather than falling back to a raw-price comparison, which would answer a different question under the same name.

Robustness on a continuous output

A "prediction flip" is meaningless when the output is continuous — every perturbation moves it, so a flip rate would be ~1.0 and every regression model would be permanently blocked.

Regression measures the mean relative prediction shift instead, against SecurityConfig.adversarial_max_relative_shift. Each feature is perturbed relative to its own magnitude, so a sum-insured column in the millions does not swamp a single-digit risk score.

shap_gap_threshold needs no rescaling

Since 0.4.2 it is relative to the mean absolute SHAP contribution, so the same default works whether contributions are probabilities or naira. On a premium model the planted geographic proxy scores around 1.4 while genuine noise sits below 0.03, against a default of 0.50.

Before 0.4.2 it was absolute and in target units, which flagged nearly every feature on a money target. If you pinned a hand-scaled value, remove it.

Worked end to end in notebook 03.