Train

Start a training job for one or more target columns.
Pass draft_model_id to continue a ready_for_training model (same row).
Each target produces its own model. Returns task IDs to poll via GET /jobs/{task_id}.

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array | null

One entry per target column to predict. Required unless draft_model_id is set. Pass a single item for standard single-target training. Pass multiple items to train one model per target, grouped under a shared group_id. Use extra_features on each item to declare which other selected targets will be available at prediction time.

ID of the uploaded dataset to train on. Required unless draft_model_id is set.

Continue training a model that is ready_for_training (created by POST /storage/datasets/{id}/recommend-preprocessing?background=true). dataset_id and target come from that row.

Optional human-readable label for this model (or group).

array | null
Defaults to []

Subset of algorithms to train and compare. Leave empty (or omit) to run all supported algorithms and auto-select the best one.

Supported algorithms:

Classification: random_forest · xgboost · lightgbm · logistic_regression · decision_tree

Regression: random_forest · xgboost · lightgbm · linear_regression · decision_tree

Winner selection: AUC-ROC 60% + F1-Macro 40% (classification) · R² 50% + Normalized-RMSE 50% (regression).

PreprocessingConfig | null

Preprocessing options applied before training. Omit on continue-train to use the stored recommendation; otherwise this is what trains.

boolean
Defaults to false

When true, automatically runs enhancement cycles after training. Diagnoses weaknesses (minority class, OOD fragility, miscalibration) and retrains with targeted fixes (SMOTE, tree-based retry, calibration). Best model wins. The stored dataset is kept so you can retrain later. Check model enhancement_status for progress: validating → enhancing → stable.

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