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name: estimators description: "Choose and use AutoSklearn estimator classes for fit, predict, refit, model inspection, temporary folders, and bounded smoke checks." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

estimators

Use this sub-skill when the user needs to choose or operate the high-level estimator API: AutoSklearnClassifier, AutoSklearnRegressor, or AutoSklearn2Classifier; fit a model; predict or predict probabilities; refit or rebuild an ensemble; inspect basic run statistics, leaderboard rows, or final models; control temporary run folders; or run a bounded local smoke helper.

Fast routing

• Binary, multiclass, or multilabel classification: use autosklearn.classification.AutoSklearnClassifier unless the user explicitly wants Auto-sklearn 2.0's hands-free classifier behavior.
• Regression or multioutput regression: use autosklearn.regression.AutoSklearnRegressor.
• Auto-sklearn 2.0 classification: use autosklearn.experimental.askl2.AutoSklearn2Classifier when the user wants ASKL2's selector/portfolio defaults and does not need explicit include, exclude, resampling_strategy, metadata_directory, or SMAC callback knobs.
• If the request is mostly about feature dtypes, feat_type, target validation, metrics, custom scorers, dataset_compression, or resampling strategies, route to data-metrics-validation.
• If the request is mostly about n_jobs, Dask, SMAC/search strategy, ensemble tuning, performance_over_time_, deep cv_results_ interpretation, or disk/performance trade-offs, route to search-and-parallelism.
• If the request is about adding classifiers, regressors, preprocessors, component IDs, or search-space implementation, route to custom-components.
• If the request is about meta-learning metadata, ASKL2 selector files, metadata_directory, or metadata refresh, route to metadata-maintenance.

Read first

• API reference for imports, observed signatures, constructor knobs, target support, and post-fit inspection methods.
• Workflows for concrete classification, regression, multilabel, multioutput, refit, ensemble, folder-control, and bounded-smoke recipes.
• Troubleshooting for import/install warnings, dummy-only ensembles, disabled-output prediction failures, temporary-folder growth, and ASKL2 cache issues.

Minimal operating checklist

1. Select the estimator class from the target type and requested behavior.
2. Set explicit bounded resources: time_left_for_this_task, per_run_time_limit, memory_limit if needed, seed, and a unique tmp_folder when logs/models should be inspectable.
3. Call fit(X_train, y_train, X_test=None, y_test=None, feat_type=None, dataset_name="short_name"). Keep dataset_name short and non-sensitive; it improves statistics and output labeling.
4. Validate outputs immediately after fit:
   • classifier: predict(X_test) shape matches expected target shape; predict_proba(X_test) values are in [0, 1] and non-multilabel rows sum to 1.
   • regressor: predict(X_test) shape is (n_samples,) or (n_samples, n_outputs) for multioutput regression.
5. Inspect the run with sprint_statistics(), leaderboard(...), and show_models() before deciding whether the result is meaningful. A dummy-only final ensemble is a troubleshooting signal, not a success.
6. Use refit(X, y) after cross-validation or when the final ensemble should be trained on all available training data. Use fit_ensemble(...) only when model/prediction outputs were saved.
7. For a safe smoke, run python sub-skills/estimators/scripts/bounded_estimator_smoke.py --help or dry-run the helper first; add --run only when a bounded AutoML fit is acceptable.

Guardrails

• Do not pass output_directory to these estimator constructors for the inspected 0.16.0dev API: it is not in the observed public estimator signatures. Prefer tmp_folder plus delete_tmp_folder_after_terminate; verify a newer installed signature before using any documented output_directory variant.
• Do not set disable_evaluator_output=True when the user expects predict, predict_proba, show_models, or post-hoc fit_ensemble to work.
• Do not reuse an old temporary run folder for a new independent fit unless the user intentionally wants to resume/inspect a prior run and the folder semantics are understood.
• Do not claim that a very small smoke budget proves model quality. It only proves that the estimator API can import, fit, inspect, and predict on a small dataset.