Data and feature engineering · MLD-1132
SMOTE
Creating minority-class feature vectors by interpolating between nearby minority examples.
Original reference page · detailed expansion pending. Original definitions, examples, notes and citations are preserved below. New examples are illustrative, not benchmark results.
Original example
Balance a tabular training set.
Original caveat
Interpolated points may not be semantically valid.
Context and aliases
Level: Intermediate. Stages: Data · Training · Evaluation.
Tags: ML · DL · SLM · LLM.
Aliases: Synthetic Minority Over-sampling Technique.
Related concepts
Concepts linking here
Original references
- scikit-learn: cross-validation ↗ · Reference link
- scikit-learn: missing-value imputation ↗ · Reference link
- scikit-learn: preprocessing ↗ · Reference link
- scikit-learn: feature selection ↗ · Reference link
- Original reference URL ↗