Question
You have a dataset with imbalanced classes. You must ensure each fold maintains a percentage of each class label. Which cross-validation technique is suitable for you?
A. Stratified K-Fold cross-validation
B. Time series cross-validation
C. Leave-one-out cross-validation
D. K-Fold cross-validation
Answer
A. Stratified K-Fold cross-validation
Explanation
When working with imbalanced datasets—where one class significantly outnumbers the others—standard cross-validation techniques like K-Fold can lead to folds that do not represent the overall class distribution. This can result in biased model evaluation, as some folds may lack sufficient representation of minority classes, skewing performance metrics.
Stratified K-Fold Cross-Validation addresses this issue by ensuring that the class distribution in each fold matches the overall class distribution of the dataset. This is achieved through stratification, which involves dividing the data such that each fold contains approximately the same proportion of samples from each class as in the original dataset.
Why Stratified K-Fold is Ideal
- Preserves Class Balance: Each fold maintains a representative proportion of classes, ensuring minority classes are not underrepresented.
- Reliable Evaluation: It provides more accurate and consistent model evaluation metrics, especially for imbalanced datasets.
- Widely Used in Practice: This method is commonly applied in scenarios like fraud detection, medical diagnosis, and rare event prediction where minority classes are critical.
Other Options Explained
B. Time Series Cross-Validation: Used for sequential data (e.g., stock prices or weather data) and does not address class imbalance.
C. Leave-One-Out Cross-Validation: Evaluates on a single sample per iteration, which is computationally expensive and does not inherently handle class imbalance.
D. K-Fold Cross-Validation: Splits data randomly into k folds without considering class proportions, leading to potential imbalance in folds.
This ensures that both training and testing sets in each fold maintain the same class distribution as the original dataset.
For imbalanced datasets requiring balanced representation across folds, Stratified K-Fold Cross-Validation is the most suitable technique. It ensures fair evaluation by maintaining consistent class proportions in every fold, making it essential for robust model validation in classification tasks with imbalanced data.
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