---

name: training-core description: "Train, configure, evaluate, and use supervised.AutoML for tabular classification and regression." disable-model-invocation: true metadata: disco-role: operating license: MIT

Training Core

Use this sub-skill when a task involves fitting, configuring, evaluating, or making predictions with supervised.AutoML for tabular binary classification, multiclass classification, or regression.

Route here for

• Choosing mode="Explain", "Perform", "Compete", or "Optuna" and making the choice safe for the user's time budget.
• Selecting algorithms, validation strategy, time limits, eval_metric, custom metrics, random state, CPU parallelism, ensembling, or stacking.
• Calling fit(), predict(), predict_proba(), predict_all(), score(), or need_retrain().
• Diagnosing training and prediction errors caused by unfitted models, invalid task/mode/algorithm/metric settings, result-directory conflicts, custom CV mistakes, or too-small time limits.
• Creating a bounded synthetic smoke check with scripts/mljar_automl_smoke.py before using user data.

Route elsewhere

• To ../data-preprocessing/ for input dtype cleanup, missing/categorical/text/datetime handling, target-shape decisions, and feature-engineering data choices.
• To ../artifacts-reports/ for saved results_path layout, leaderboard/report interpretation, structured reports, explainability artifacts, and save/load workflows.
• To ../fairness-workflows/ for sensitive_features, fairness metrics, thresholds, and privileged/underprivileged groups.
• To ../app-deployment/ for app(), local_app(), and publish_app() workflows.

Operating checklist

1. Confirm the package imports as supervised and use from supervised import AutoML.
2. Identify the ML task: let ml_task="auto" infer from target values only when that inference is safe; otherwise set binary_classification, multiclass_classification, or regression explicitly.
3. Pick a mode and budget. Prefer Explain plus a short algorithm list for exploration, Perform for production-style tabular models, Compete only when extra search is justified, and Optuna only with an explicit per-algorithm optuna_time_budget.
4. Choose a clean results_path. It may be new or empty for training; an existing trained run with params.json is loaded instead of treated as scratch output.
5. Fit with automl.fit(X, y, sample_weight=..., cv=...) as needed. Use custom cv only with validation_strategy={"validation_type": "custom"}.
6. Use predict() for labels or regression values, predict_proba() only for classification probabilities, predict_all() for a DataFrame containing labels/probabilities or regression predictions, and score(X, y) for accuracy/R².
7. Use need_retrain(X_new, y_new, decrease=...) as a quick degradation signal, not as a substitute for full drift monitoring.

Read next

• API reference for signatures, accepted values, return shapes, and task-specific metric rules.
• Workflows for binary, multiclass, regression, custom validation, scoring, and retraining recipes.
• Advanced configuration for modes, time limits, ensembling/stacking, Optuna, custom metrics, and budget controls.
• Troubleshooting for common training, prediction, configuration, and dependency failures.
• scripts/mljar_automl_smoke.py for a no-network synthetic helper that can run from any working directory.
• Root package assumptions: ../../references/package-overview.md; cross-cutting install/import issues: ../../references/troubleshooting.md.