Use this implementation to study or prototype the named learning or search method with explicit features, labels, model parameters, and reproducible inputs.

Binary features

Required list input.

Labels

Required list input.

Feature names

Required list input.

Prior alpha

Required number input.

Prior beta

Required number input.

Rank binary feature rules by posterior positive-rate separation with Beta priors. The tool evaluates the supplied inputs together and returns the named outputs below; it does not infer omitted operating conditions or change the units shown.^[1]

Ranked rules

The resulting ranked rules returned as an object.

• Model behavior depends on data quality, preprocessing, initialization, hyperparameters, optimization, leakage, class balance, distribution shift, and implementation details. Passing an example does not establish generalization, fairness, robustness, or production suitability.
• Use finite inputs in the displayed units and preserve more precision than the final presentation requires. Independently verify safety-critical, financial, compliance, or production decisions.

Evaluate on held-out and stress-test data, compare baselines, report uncertainty and resource cost, and preserve training and preprocessing provenance.

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