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name: feature-workflows description: "Use mlxtend feature selection, feature extraction, preprocessing, and transaction encoding workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Feature workflows

Use this sub-skill when the task is to prepare model inputs with mlxtend feature selectors, extraction transformers, transaction one-hot encoders, or preprocessing helpers.

Start here

• Use references/api-reference.md for verified signatures, attributes, and parameter constraints.
• Use references/workflows.md for selector, extraction, preprocessing, and transaction encoding recipes.
• Use references/data-formats.md for accepted ndarray/DataFrame/list-of-lists schemas and metric/one-hot output shapes.
• Use references/troubleshooting.md before changing code when selectors, scorers, column names, sparse data, or transaction encodings fail.
• Run scripts/feature_workflows_smoke.py for a deterministic CPU smoke check of the installed APIs.

Owned APIs

• Feature selection: SequentialFeatureSelector, ExhaustiveFeatureSelector, ColumnSelector.
• Feature extraction: PrincipalComponentAnalysis, LinearDiscriminantAnalysis, RBFKernelPCA.
• Preprocessing: TransactionEncoder, DenseTransformer, MeanCenterer, CopyTransformer, one_hot, minmax_scaling, standardize, shuffle_arrays_unison.

Boundaries

• After TransactionEncoder produces one-hot transaction data, route itemset mining and association rules to ../frequent-patterns/SKILL.md.
• Route estimator/ensemble construction details to ../estimators-and-ensembles/SKILL.md; this sub-skill only shows how estimators are passed into selectors.
• Route plotting of SFS/EFS metric dictionaries, PCA graphics, and other visualization tasks to ../plotting-and-utilities/SKILL.md.

Operating rules

1. Treat selectors as sklearn-style transformers: fit on X, y, inspect selected-feature attributes, then call transform or fit_transform to reduce X.
2. Prefer pandas DataFrame inputs when feature names, string fixed_features, or string feature_groups matter; use ndarray integer indices otherwise.
3. Keep feature_groups exhaustive and non-overlapping. If fixed_features intersects a group, include all members of that group in fixed_features.
4. Use ExhaustiveFeatureSelector only for small feature/group counts; it evaluates all combinations between min_features and max_features.
5. Keep sparse data sparse until an API requires dense arrays; use DenseTransformer deliberately because densification can multiply memory use.
6. Keep transaction data as list[list[item]] until TransactionEncoder; preserve encoder.columns_ when building pandas one-hot DataFrames.

Safe smoke

From this sub-skill directory or any environment with mlxtend installed:

python scripts/feature_workflows_smoke.py --task all

The smoke script uses tiny in-memory sklearn/numpy/pandas examples, writes no files, does not download data, and does not depend on a source checkout.