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name: imbalanced-learn description: "Router for imbalanced-learn samplers, workflows, metrics, datasets, and balanced batch generators." metadata: disco-role: operating disable-model-invocation: true license: MIT

imbalanced-learn

Use this repo skill for the imbalanced-learn package when the task is about class imbalance, resampling, imbalance-aware pipelines, evaluation metrics, or balanced mini-batches for Keras/TensorFlow.

The import root is imblearn. The package is scikit-learn compatible and is centered on fit_resample workflows for tabular and scientific-python style data.

Quick start

• If you need the package API, start with this router and then read the relevant sub-skill.
• If you need a fast sanity check, run scripts/package_smoke.py.
• If you need live API names or signatures from the installed environment, run scripts/inspect_signatures.py.

Minimal import check

from imblearn import FunctionSampler
from imblearn.over_sampling import RandomOverSampler

If that import fails, read references/troubleshooting.md before assuming the repo skill is stale.

How to choose a sub-skill

sampling-algorithms

Use this when the request mentions:

• RandomOverSampler, SMOTE, ADASYN, SMOTENC, SMOTEN
• RandomUnderSampler, TomekLinks, EditedNearestNeighbours, NearMiss
• ClusterCentroids, SMOTEENN, SMOTETomek
• FunctionSampler, sampling_strategy, check_sampling_strategy
• sparse, pandas, heterogeneous, or categorical feature handling during resampling

Read sub-skills/sampling-algorithms/SKILL.md first.

model-workflows

Use this when the request mentions:

• Pipeline or make_pipeline
• leakage-safe resampling with classifiers
• BalancedBaggingClassifier, BalancedRandomForestClassifier
• EasyEnsembleClassifier, RUSBoostClassifier
• InstanceHardnessCV

Read sub-skills/model-workflows/SKILL.md first.

evaluation-and-data

Use this when the request mentions:

• make_imbalance, fetch_datasets
• classification_report_imbalanced, geometric_mean_score
• sensitivity/specificity metrics, MA-MAE, or ValueDifferenceMetric
• data-leakage pitfalls and dataset-balancing examples

Read sub-skills/evaluation-and-data/SKILL.md first.

optional-batch-generators

Use this when the request mentions:

• balanced_batch_generator
• BalancedBatchGenerator
• Keras or TensorFlow mini-batch workflows for imbalanced data

Read sub-skills/optional-batch-generators/SKILL.md first.

Cross-cutting rules

• Resample only the training split, not the entire dataset, when the user is trying to avoid leakage.
• Preserve sparse inputs as CSR-compatible workflows where possible, but know that many samplers return dense output.
• Treat pandas support as first-class when the user provides DataFrames.
• Treat TensorFlow/Keras support as optional: useful, but not required for the core CPU package.
• When a workflow needs the exact constructor or signature of a public class or function, consult the nearest reference file or run scripts/inspect_signatures.py.

Read these files when needed

• references/api-overview.md for the compact module and symbol map.
• references/troubleshooting.md for install, import, dtype, sparse, leakage, network, and optional-backend issues.
• references/repo-provenance.md for the source snapshot that produced this skill.
• references/repo-routing-metadata.json for managed-router placement data.

Root scripts

• scripts/package_smoke.py for a small end-to-end CPU and optional-backend smoke check.
• scripts/inspect_signatures.py for live API introspection from the installed environment.

What this skill does not do

• It does not replace the sub-skill-level workflow guides.
• It does not ask you to run the original repository examples as runtime skill instructions.
• It does not claim GPU-specific functionality; the package is CPU-oriented, with optional TensorFlow/Keras dependencies.