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    Interface PredictiveStudioBuilderPayload

    The agent payload = the modeling plan the conversation accumulated, plus the builder's outcome. Leaderboard is the builder's own row shape, whose score may be null (an unmeasured candidate), so it replaces the plan's.

    interface PredictiveStudioBuilderPayload {
        Approved?: boolean;
        BestModel?: { ID?: string; Name?: string; Version?: string | number };
        BestModelID?: string;
        BuildAttemptUserMessageCount?: number;
        BuildResult?: PredictiveStudioBuildOutcome;
        CandidateFeatures: {
            Kind: CandidateFeatureKind;
            Name: string;
            SourceRef: string;
            Why: string;
        }[];
        CandidateSources: {
            Kind: | "Entity"
            | "Query"
            | "ExternalEntity"
            | "VectorSet"
            | "FeaturePipeline";
            Ref: string;
            Why: string;
        }[];
        FeatureImportance?: MLFeatureImportancePayload[];
        Goal: string;
        Leaderboard?: MLLeaderboardEntryPayload[];
        LeakageNotes: { Action: "exclude"
        | "allow"; Field: string; Risk: string }[];
        Markdown?: string;
        Name?: string;
        ProposedBudget: {
            MaxComputeCost?: number;
            MaxRuns?: number;
            MaxWallclockMinutes?: number;
        };
        ProposedExperiments: {
            AlgorithmName: string;
            FeatureSet: string[];
            Hyperparameters?: Record<string, unknown>;
            Label: string;
            Priority: number;
            Rationale: string;
        }[];
        Summary?: string;
        TargetDefinition: {
            AsOfStrategy?: {
                Column?: string;
                Mode: "none"
                | "column"
                | "offset";
                OffsetDays?: number;
            };
            EntityName: string;
            ProblemType: "classification"
            | "regression";
            SuccessMetric: string;
            TargetVariable: string;
        };
        TargetMetric?: string;
        ValidationStrategy: ValidationStrategy;
    }

    Hierarchy

    Index

    Properties

    Approved? BestModel? BestModelID? BuildAttemptUserMessageCount? BuildResult? CandidateFeatures CandidateSources FeatureImportance? Goal Leaderboard? LeakageNotes Markdown? Name? ProposedBudget ProposedExperiments Summary? TargetDefinition TargetMetric? ValidationStrategy

    Properties

    Approved?: boolean

    User approval gate — execution does not begin until this is true.

    BestModel?: { ID?: string; Name?: string; Version?: string | number }

    Best model pointer for MLExperimentResultsSpec

    BestModelID?: string

    Convenience scalar best model pointer

    BuildAttemptUserMessageCount?: number

    The count of USER messages in the conversation at the moment the orchestrator last FORCED a build (stamped by PredictiveStudioModelDevAgent.determineNextStep). After a failed build this is what distinguishes the stale "build it" message that triggered the failed attempt (same count → no re-force, the no-loop guard) from a FRESH user request to retry (higher count → deterministic rebuild).

    BuildResult?: PredictiveStudioBuildOutcome
    CandidateFeatures: {
        Kind: CandidateFeatureKind;
        Name: string;
        SourceRef: string;
        Why: string;
    }[]

    Candidate features proposed by the Data Scout, each with rationale.

    CandidateSources: {
        Kind:
            | "Entity"
            | "Query"
            | "ExternalEntity"
            | "VectorSet"
            | "FeaturePipeline";
        Ref: string;
        Why: string;
    }[]

    Candidate feed-in sources proposed by the Data Scout, each with rationale.

    FeatureImportance?: MLFeatureImportancePayload[]

    Feature importance entries for MLExperimentResultsSpec

    Goal: string

    Business objective, refined from the user's initial goal.

    Leaderboard?: MLLeaderboardEntryPayload[]

    Ranked leaderboard entries for MLExperimentResultsSpec

    LeakageNotes: { Action: "exclude" | "allow"; Field: string; Risk: string }[]

    Leakage risks identified by the Data Scout and the chosen action per field.

    Markdown?: string

    Full markdown results narrative for MLExperimentResultsSpec

    Name?: string

    Optional display name

    ProposedBudget: {
        MaxComputeCost?: number;
        MaxRuns?: number;
        MaxWallclockMinutes?: number;
    }

    Proposed resource budget for the experiment session.

    ProposedExperiments: {
        AlgorithmName: string;
        FeatureSet: string[];
        Hyperparameters?: Record<string, unknown>;
        Label: string;
        Priority: number;
        Rationale: string;
    }[]

    Ranked experiments proposed by the Experiment Designer (feature combos × algorithms × hyperparameters).

    Summary?: string

    Plain-language summary of results for MLExperimentResultsSpec

    TargetDefinition: {
        AsOfStrategy?: {
            Column?: string;
            Mode: "none" | "column" | "offset";
            OffsetDays?: number;
        };
        EntityName: string;
        ProblemType: "classification"
        | "regression";
        SuccessMetric: string;
        TargetVariable: string;
    }

    Precise definition of what is being predicted.

    Type Declaration

    • OptionalAsOfStrategy?: { Column?: string; Mode: "none" | "column" | "offset"; OffsetDays?: number }

      Optional point-in-time assembly strategy.

    • EntityName: string

      Training-unit entity (e.g. "Members").

    • ProblemType: "classification" | "regression"

      Classification or regression.

    • SuccessMetric: string

      The deterministic success metric driving the search.

    • TargetVariable: string

      Label expression/column.

    TargetMetric?: string

    Target metric name

    ValidationStrategy: ValidationStrategy

    Validation strategy for the search.

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