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name: model-workflows description: "Router for imbalanced-learn pipelines, leakage-safe resampling, balanced ensembles, and instance-hardness cross-validation." metadata: disco-role: operating disable-model-invocation: true license: MIT

model-workflows

Use this sub-skill when the request is about building a model workflow around imbalanced-learn samplers.

This sub-skill owns the package surfaces that combine resampling with model training and validation:

• Pipeline and make_pipeline
• BalancedBaggingClassifier
• BalancedRandomForestClassifier
• EasyEnsembleClassifier
• RUSBoostClassifier
• InstanceHardnessCV

What to do first

1. Decide whether the user wants a pipeline, an ensemble, or a specialized cross-validation splitter.
2. Split the data first and keep resampling on the training side.
3. Check whether the final estimator supports fit, predict, and optionally predict_proba if the workflow uses InstanceHardnessCV.
4. If the workflow chains transforms and samplers, remember that imbalanced-learn pipeline semantics differ from plain scikit-learn in fit_transform.
5. Verify the assembled workflow on a tiny synthetic dataset before scaling up.

Typical routing cues

• fit_resample inside Pipeline
• leakage-safe cross-validation
• BalancedBaggingClassifier, BalancedRandomForestClassifier
• EasyEnsembleClassifier, RUSBoostClassifier
• InstanceHardnessCV
• transform_input, memory, sample_weight

When to read the bundled references

• references/workflows.md for safe pipeline patterns and decision points.
• references/api-reference.md for the compact workflow API catalog.
• references/troubleshooting.md when cross-validation, metadata routing, or estimator compatibility fails.

Common choices

• Use Pipeline or make_pipeline when the main goal is to avoid leakage and let resampling happen only during fit.
• Use BalancedBaggingClassifier when a bagging-style ensemble should resample each bootstrap subset.
• Use BalancedRandomForestClassifier when the user wants a random-forest-like ensemble with balanced bootstrap samples.
• Use EasyEnsembleClassifier when the task wants an AdaBoost-based ensemble on balanced subsets.
• Use RUSBoostClassifier when the task wants boosting with under-sampling between iterations.
• Use InstanceHardnessCV when the task is binary classification and the user wants hard samples distributed across folds.

Native evidence to keep in mind

These repo tests are the most relevant later verification anchors for this sub-skill:

• imblearn/tests/test_pipeline.py::test_make_pipeline
• imblearn/tests/test_pipeline.py::test_pipeline_methods_pca_svm
• imblearn/ensemble/tests/test_bagging.py::test_balanced_bagging_classifier
• imblearn/ensemble/tests/test_forest.py::test_balanced_random_forest
• imblearn/ensemble/tests/test_easy_ensemble.py::test_easy_ensemble_classifier
• imblearn/ensemble/tests/test_weight_boosting.py::test_rusboost
• imblearn/model_selection/tests/test_split.py::test_default_params

Package-specific cautions

• Pipeline.fit_transform(X, y) can resample, while fit(X, y) followed by transform(X) does not. Do not assume scikit-learn equivalence.
• InstanceHardnessCV only supports binary classification and needs an estimator with predict_proba.
• Some ensembles accept sampler/sampling_strategy/replacement parameters that change the internal balancing behavior; inspect the exact constructor.
• sample_weight support depends on the specific estimator.

Use the scripts

• scripts/pipeline_leakage_check.py for a tiny leakage-safe pipeline smoke.
• scripts/ensemble_smoke.py for a representative balanced-ensemble check.
• scripts/model_selection_smoke.py for an InstanceHardnessCV sanity check.