We model stochastic choice as environment-dependent switching among a small library of deterministic decision rules. A Random Rule Model generates menu-level choice probabilities via named, interpretable rules weighted by observable menu characteristics. Identification has a two-step structure: within-feature decisive-side variation identifies relative rule weights; cross-feature richness identifies the gate. Applied to binary lottery choices, the estimated weights concentrate on a small subset of rules and shift systematically with complexity and dispersion asymmetry. The model closes nearly all of the prediction gap to a flexible neural-network benchmark, while remaining interpretable, restrictive under permutation diagnostics, and portable to an independent dataset.