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name: mljar-supervised description: "Use MLJAR AutoML for tabular classification, regression, fairness-aware training, reports, persistence, and generated Mercury apps." disable-model-invocation: true metadata: disco-role: operating license: MIT

MLJAR Supervised

Use this repo skill when a task involves mljar-supervised, supervised.AutoML, MLJAR AutoML, automated tabular machine learning, MLJAR reports, fairness-aware AutoML, or generated Mercury prediction apps.

MLJAR Supervised trains supervised AutoML pipelines for tabular binary classification, multiclass classification, and regression. It handles common preprocessing, model search, reports, explainability outputs, fairness options, persistence, and app generation through the Python package imported as supervised.

Start here

1. Confirm the package is installed and importable:

   from supervised import AutoML
   print(AutoML)
   

   For a more detailed local check, run scripts/check_mljar_supervised_install.py.

2. Identify whether the user wants to train a model, prepare data, inspect an existing run, add fairness constraints, or build an app.

3. Route to the focused sub-skill below before giving detailed API, data, report, or deployment instructions.

4. Keep user examples bounded: disable expensive feature engineering and explanations for smoke checks; use larger Perform, Compete, or Optuna runs only when the user accepts the runtime.

Route map

User intent	Read
Train/configure AutoML, choose mode, algorithms, validation, metrics, time limits, ensembling/stacking, predict/score/retrain	training-core
Prepare X, y, sample_weight, custom CV, missing/categorical/text/datetime columns, target inference, preprocessing and feature-engineering flags	data-preprocessing
Inspect results_path, load saved runs, use leaderboards, reports, structured reports, explainability artifacts, SHAP/importance/learning curves, persistence	artifacts-reports
Configure fairness-aware training with sensitive_features, fairness metrics, thresholds, privileged/underprivileged groups, fairness reports	fairness-workflows
Generate app files, preview locally with Mercury, or prepare guarded publishing with app(), local_app(), publish_app()	app-deployment

Package assumptions

• Distribution name: mljar-supervised; import name: supervised.
• Main public class: supervised.AutoML.
• Supported ML tasks: binary classification, multiclass classification, and regression.
• Built-in modes: Explain, Perform, Compete, and Optuna.
• Common algorithms include Baseline, Linear, Decision Tree, Random Forest, Extra Trees, LightGBM, Xgboost, CatBoost, Neural Network, and Nearest Neighbors.
• Core workflows are CPU-compatible. GPU hardware is not required for this skill's selected scope.
• Optional surfaces can need extra system/runtime setup: Graphviz for some tree visualizations and Mercury plus browser/network/auth for local or hosted apps.

Read references/package-overview.md for install prerequisites, high-level capabilities, optional dependencies, and safe smoke-test strategy. Read references/troubleshooting.md for cross-cutting install/import/dependency failures.

Common operating patterns

• For a quick user-data smoke, use AutoML(mode="Explain", algorithms=["Baseline", "Decision Tree"], explain_level=0, train_ensemble=False, stack_models=False, golden_features=False, features_selection=False, total_time_limit=30) and a disposable results_path.
• For production-style tabular modeling, start from mode="Perform", set a realistic total_time_limit, and keep results_path stable for later reports and reload.
• For competition or maximum-performance work, use mode="Compete" or mode="Optuna" only after discussing runtime, validation, stacking, and per-algorithm tuning budgets.
• For saved runs, load the full AutoML results directory with AutoML(results_path="..."); do not load learner files directly unless a sub-skill explicitly says why.
• For fairness, pass sensitive features to fit(..., sensitive_features=...) and choose task-compatible fairness metrics.
• For generated apps, call automl.app() to create files first; do not start Mercury or publish without explicit user approval.

Bundled checks

• scripts/check_mljar_supervised_install.py: import/version/signature and optional dependency probe.
• sub-skills/training-core/scripts/mljar_automl_smoke.py: tiny synthetic training/prediction smoke.
• sub-skills/data-preprocessing/scripts/inspect_preprocessing_behaviour.py: lightweight preprocessing utility checks.
• sub-skills/artifacts-reports/scripts/structured_report_smoke.py: tiny structured-report and reload smoke.
• sub-skills/fairness-workflows/scripts/fairness_smoke.py: tiny fairness-aware fit and report signal check.
• sub-skills/app-deployment/scripts/generate_app_workspace_smoke.py: tiny app-workspace generation check without serving or publishing.

Refresh and provenance

Read references/repo-provenance.md before deciding whether this skill is current for a checkout. If the package version, commit, public AutoML signatures, docs, examples, or test-backed behavior changed, run refresh-repo-skill instead of relying on this snapshot.