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    Interface MLInferenceResultPayload

    The structured payload a successful scoring run carries on its RecordResult.

    interface MLInferenceResultPayload {
        badgeColor?: "green" | "amber" | "red" | "blue" | "gray";
        band?: string;
        class?: string;
        drivers?: { feature: string; value: number }[];
        icon?: string;
        modelId: string;
        problemType: ProblemType;
        score: number;
        scoredAt: string;
        scoreLabel?: string;
        status?: string;
        statusLabel?: string;
        target: string;
    }
    Index

    Properties

    badgeColor? band? class? drivers? icon? modelId problemType score scoredAt scoreLabel? status? statusLabel? target

    Properties

    badgeColor?: "green" | "amber" | "red" | "blue" | "gray"

    Semantic badge color for UI rendering: 'green' | 'amber' | 'red' | 'blue' | 'gray'.

    band?: string

    Normalized status tier key (e.g. "high", "medium", "low").

    class?: string

    Predicted class label, present for classification problems.

    drivers?: { feature: string; value: number }[]

    Top signed per-record drivers behind this prediction (P1-5), present only for models with exact per-row attribution (linear). Feature names are post-preprocessing output columns; UIs collapse/humanize them. value > 0 pushed the score up, < 0 down. Absent for tree/ensemble/multiclass models.

    icon?: string

    FontAwesome icon name (e.g. 'fa-circle-check', 'fa-triangle-exclamation').

    modelId: string

    The model that produced the prediction (lineage).

    problemType: ProblemType

    Classification or regression.

    score: number

    Numeric model output: probability (classification) or value (regression).

    scoredAt: string

    ISO-8601 UTC timestamp of when this prediction was produced. Lets write-back persist a dedicated "last scored at" column on the target entity via OutputMapping ({ "LastScoredAt": "$.scoredAt" }) — distinct from the row's whole-record __mj_UpdatedAt, which moves on any edit, not just scoring.

    scoreLabel?: string

    Display label for the score column (e.g. "Renewal Probability", "Default Risk").

    status?: string

    Qualitative status label evaluated against model OutcomeConfig (e.g. "High", "Low Risk", "Severe").

    statusLabel?: string

    Display label for the status column (e.g. "Renewal Status", "Risk Level").

    target: string

    The target/label the model predicts.

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