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    Interface ValidationConfig

    Validation configuration sent to the sidecar at train time (plan §3.2 / §8.2). The default is a single train/test split. holdout uses the same out-of-sample split (it reads test_size too); kfold scores pooled out-of-fold predictions. Every strategy reports out-of-sample metrics.

    interface ValidationConfig {
        holdout_size?: number;
        k?: number;
        random_state?: number;
        strategy: "train_test_split" | "kfold" | "holdout";
        test_size?: number;
    }
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    holdout_size? k? random_state? strategy test_size?

    Properties

    holdout_size?: number

    Locked-holdout fraction the sidecar should re-carve from the training data and score exactly once (plan §8.2). This is the fallback path used when the orchestrator does not forward an explicit TrainRequest.holdout matrix — e.g. the sidecar's own pytest fixtures. The production orchestrator carves the locked holdout in TypeScript and forwards the exact rows via TrainRequest.holdout instead (which takes precedence), so the holdout is auditable and the carve is deterministic. Omitted ⇒ no sidecar-side re-carve.

    k?: number

    Number of folds for kfold.

    random_state?: number

    Random seed for the sidecar's holdout / train-test splits (default 42).

    strategy: "train_test_split" | "kfold" | "holdout"

    Validation strategy the sidecar should apply during /train.

    test_size?: number

    Validation fraction for the split that train_test_split and holdout both use (e.g. 0.2).

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