Machine Learning Section 013

Imbalanced, Multi-class and Multi-label

What changes when one class is rare, when there are twenty classes, or when a row can have several answers at once.

11 of 11 lessons published Three reading levels on every lesson

Start with “SMOTE and its variants”

Lessons in order

Work top to bottom. Each lesson assumes the one above it.

  1. SMOTE and its variants
  2. Undersampling strategies
  3. Class weights
  4. Resampling inside cross-validation
  5. Balanced bagging and EasyEnsemble
  6. Lift and gain charts
  7. One-vs-rest and one-vs-one
  8. Macro, micro and weighted averaging
  9. Multi-label classification
  10. Classifier chains
  11. Ordinal targets