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name: sklearn-tuning description: "Routes KerasTuner SklearnTuner workflows for conditional scikit-learn estimator factories, cross-validation, scoring, groups, sample weights, DataFrame inputs, and pickle-backed model recovery." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Scikit-learn tuning

Use this sub-skill when the task is about keras_tuner.SklearnTuner, tuning a scikit-learn estimator factory, cross-validating several estimator families, or restoring a tuned estimator from a KerasTuner trial. This route is for scikit-learn models, not Keras neural-network training.

Include here

• SklearnTuner(oracle, hypermodel, scoring=None, metrics=None, cv=None, **kwargs).
• Conditional HyperParameters factories that return classifiers, regressors, or scikit-learn Pipeline objects.
• A consistent explicit scorer when model families have different default score semantics.
• Custom cross-validation splitters, groups, and sample_weight.
• NumPy and optional pandas DataFrame inputs.
• get_best_models() / load_model() recovery of model.pickle artifacts.
• Diagnosing invalid input types and absent optional dependencies.

Exclude or route elsewhere

• Keras model training, neural-specific callbacks, and epoch-budget search → the parent tuning route.
• Distributed chief/worker coordination → the distributed-tuning route.
• Image hypermodel constructors → the image-hypermodels route.
• Hyperband: it is a neural-training oracle and is not an appropriate Oracle for SklearnTuner.

Fast route

1. Confirm scikit-learn is installed; install pandas too if DataFrames are required.
2. Build one estimator per trial from hp, using conditional scopes for branch-specific hyperparameters.
3. Create a non-neural Oracle with objective keras_tuner.Objective("score", "max"); Bayesian optimization is a good default for a small space.
4. Supply an explicit scoring callable when comparing model families, then select cv and pass groups when the splitter requires them.
5. Call search(X, y, sample_weight=..., groups=...) with NumPy arrays or pandas DataFrames and inspect trial status/metrics.
6. Restore the winner with get_best_models() and validate it on held-out data. See workflows.

Read next

• API reference for exact signatures and implementation-backed behavior.
• Workflows for factory, scoring, CV, weighting, DataFrame, and restore patterns.
• Troubleshooting for dependency and input failures.
• From the skill root, run sub-skills/sklearn-tuning/scripts/smoke_sklearn.py for a tiny dependency-aware local check.

Operating cautions

• cv=None means KFold(5, shuffle=True, random_state=1).
• sample_weight is split per fold. It is passed to an estimator's fit only when fit advertises sample_weight; it is omitted for a scikit-learn Pipeline and estimators without that argument.
• The tuner saves only the model produced by the final CV fold for each trial, although the trial score is the mean over folds. Treat it as the refit model only after checking the intended deployment workflow.
• DataFrame support depends on pandas being importable when the tuner module is loaded. Plain lists are not accepted as X or y.