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name: tabular-automl description: "Guide Auto-PyTorch tabular classification and regression workflows." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Tabular AutoML

Use this sub-skill for Auto-PyTorch tabular classification and regression. It covers the main public search workflow, single-configuration fitting, refitting, feature typing, portfolio selection, and result inspection.

When to use

Choose this route when the user asks to:

• train or tune tabular classification or regression models
• handle categorical and numerical columns in pandas or NumPy data
• control resampling, budgets, or search-space restrictions
• force or inspect traditional learners
• inspect the final ensemble, run history, or performance over time
• fit one configuration instead of running a full search

What this route owns

• TabularClassificationTask
• TabularRegressionTask
• TabularClassificationPipeline
• TabularRegressionPipeline
• TraditionalTabularClassificationPipeline
• TraditionalTabularRegressionPipeline
• TabularInputValidator
• TabularFeatureValidator
• TabularTargetValidator
• get_dataset_requirements(...)
• get_configuration_space(...)
• tabular search, fit, refit, predict, score, and inspection flows

Main workflow

1. Decide whether the task is classification or regression.
2. Validate the inputs and, when needed, pass feat_types for ambiguous dtypes.
3. Build the task or pipeline object.
4. Choose the search style:
   • search(...) for end-to-end AutoML
   • fit_pipeline(...) for one configuration
   • refit(...) after search when you want to retrain on the full dataset
5. Inspect the outcome with predict(...), score(...), show_models(), and sprint_statistics().

Common decisions

Data shape

• Accepts pandas DataFrames, NumPy arrays, and Python lists in the supported tabular formats.
• Categorical detection is easiest with DataFrames.
• If NumPy data encodes categories as integers, pass feat_types explicitly.

Search control

• Use include_components to narrow the search to a known subset.
• Use exclude_components to rule out components you do not want.
• Use search_space_updates for targeted hyperparameter overrides.
• Use portfolio_selection='greedy' when you want the built-in warm-start portfolio.
• Keep enable_traditional_pipeline=True when you want the traditional learner baseline alongside neural pipelines.

Resampling

• Default holdout validation is the common starting point.
• Cross-validation and stratified variants are available when you need more stable evaluation.
• fit_pipeline(...) accepts the same family of resampling controls.

Traditional learners

The traditional path can use:

• LightGBM
• CatBoost
• Random Forest
• Extra Trees
• SVM
• KNN

These remain useful when you want a fast non-neural baseline or a smaller dataset path.

What to read next

• references/workflows.md for end-to-end tabular recipes, including search, fit-pipeline, refit, and result inspection
• references/api-reference.md for the main class and method signatures
• references/troubleshooting.md for install/import, data-shape, and optional-dependency problems
• scripts/tabular_smoke.py for a tiny fixture check of tabular validation and search-space setup

Do not use this route for

• time series forecasting
• image classification proof-of-concept paths
• repo-maintenance or CI workflows

Route forecasting questions to sub-skills/forecasting/ instead.