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name: sampling-algorithms description: "Router for imbalanced-learn over-sampling, under-sampling, combine samplers, and custom resampling primitives." metadata: disco-role: operating disable-model-invocation: true license: MIT

sampling-algorithms

Use this sub-skill when the task is about choosing, configuring, or debugging an imbalanced-learn sampler.

This sub-skill owns the package families that directly change class counts or sample composition:

• over-sampling: RandomOverSampler, ADASYN, SMOTE, SMOTENC, SMOTEN, BorderlineSMOTE, KMeansSMOTE, SVMSMOTE
• under-sampling: RandomUnderSampler, TomekLinks, EditedNearestNeighbours, RepeatedEditedNearestNeighbours, AllKNN, OneSidedSelection, CondensedNearestNeighbour, NeighbourhoodCleaningRule, NearMiss, ClusterCentroids, InstanceHardnessThreshold
• combine samplers: SMOTEENN, SMOTETomek
• custom resampling: FunctionSampler

What to do first

1. Decide whether the request is about increasing minority support, reducing majority support, cleaning borderline samples, or writing a custom sampler.
2. Identify the input shape: dense NumPy array, sparse matrix, pandas DataFrame, mixed object array, or all-categorical data.
3. Check whether the workflow needs categorical handling, neighbor-based synthesis, or a simple bootstrap-style resampler.
4. Fit only on the training branch; do not resample the full dataset before a split.
5. Verify the result with a tiny count check before moving on.

Typical routing cues

• sampling_strategy
• fit_resample
• random_state, replacement, shrinkage
• k_neighbors, m_neighbors, n_neighbors
• SMOTE, ADASYN, NearMiss, TomekLinks, SMOTEENN
• FunctionSampler
• pandas or sparse input support during resampling
• categorical features or all-categorical samples

When to read the bundled references

• references/workflows.md for the decision tree from task shape to sampler family.
• references/api-reference.md for the compact public sampler catalog and key signatures.
• references/troubleshooting.md when neighbor counts, dtype errors, sparse output, or categorical feature handling fail.

Common choices

• Use RandomOverSampler when the request is about a simple, duplicate-based bootstrap.
• Use SMOTE or one of its variants when the request needs synthetic minority samples.
• Use SMOTENC for mixed numeric/categorical features.
• Use SMOTEN when all features are categorical.
• Use RandomUnderSampler when the request needs a fast majority reduction.
• Use TomekLinks, EditedNearestNeighbours, RepeatedEditedNearestNeighbours, AllKNN, or NeighbourhoodCleaningRule when the request is about cleaning noisy or borderline observations.
• Use ClusterCentroids when the task wants prototype generation rather than simply selecting existing samples.
• Use SMOTEENN or SMOTETomek when the task wants a packaged over/under combination.
• Use FunctionSampler when the user already knows the exact custom logic and only needs a thin sampler wrapper.

Native evidence to keep in mind

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

• imblearn/over_sampling/tests/test_random_over_sampler.py::test_ros_fit_resample
• imblearn/over_sampling/_smote/tests/test_smote.py::test_sample_regular
• imblearn/over_sampling/_smote/tests/test_smote_nc.py::test_smotenc_pandas
• imblearn/under_sampling/_prototype_selection/tests/test_random_under_sampler.py::test_rus_fit_resample
• imblearn/under_sampling/_prototype_selection/tests/test_tomek_links.py::test_tl_fit_resample
• imblearn/combine/tests/test_smote_enn.py::test_sample_regular
• imblearn/tests/test_base.py::test_function_sampler_func

Package-specific cautions

• SMOTE-family methods need a sensible neighbor configuration and enough minority samples.
• SMOTENC needs a categorical feature specification that matches the input layout.
• SMOTEN is for categorical-only data, not mixed numeric/categorical data.
• RandomOverSampler(shrinkage=...) is numeric-data only.
• ClusterCentroids may densify or become inefficient on sparse data.
• FunctionSampler should remain small and explicit; it is not a replacement for a full pipeline.

Use the scripts

• scripts/sampler_smoke.py for a tiny representative resampling smoke.
• scripts/sampling_strategy_demo.py for small examples of dict and callable sampling strategies.