Multiclass stratified CV

Definition

• Multiclass Stratified CV is a cross-validation method for multiclass classification problems where each fold maintains approximately the same class distribution as the original dataset.
• It’s an extension of Stratified CV for binary classification to cases with more than two classes.

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How It Works

1. Calculate the overall class distribution in the dataset.
   • Example: Class A = 60%, Class B = 30%, Class C = 10%.
2. When splitting into k folds, ensure each fold has roughly the same ratio.
   • Each fold should contain A: 60%, B: 30%, C: 10%.
3. Guarantees that both training and validation sets are representative of all classes.

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Example

• Dataset = 1,000 samples (A = 600, B = 300, C = 100).
• k = 5 folds.
• Regular k-fold: some folds may have very few Class C samples.
• Multiclass Stratified k-fold:
  • Each fold → A = 120, B = 60, C = 20 → balanced like the original distribution.

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Why It Matters

• In imbalanced multiclass datasets, regular k-fold may underrepresent minority classes in some folds.
• Multiclass stratification ensures fair and stable evaluation of the model.
• Prevents misleading performance metrics (e.g., if one fold has almost no samples of a class).

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When to Use

• Any multiclass classification problem (3+ classes).
• Especially important when class imbalance exists.
• Not used for regression problems (stratification applies only to classification).

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Summary
Multiclass Stratified CV = k-fold cross-validation where each fold preserves the class distribution across multiple classes.

• Ensures every fold contains all classes in the right proportions.
• Provides more reliable and fair model evaluation.