What Cross-Validation Meaning, Applications & Example
Technique to assess model performance by rotating training and validation sets.
What is Cross-Validation?
Cross-Validation is a technique used to assess the performance and generalizability of a machine learning model . It involves dividing the dataset into multiple subsets (folds), training the model on some folds, and testing it on the remaining fold. This process is repeated several times to ensure the model’s robustness and to reduce overfitting .
Types of Cross-Validation
- K-Fold Cross-Validation: Divides the data into K equal-sized folds, and the model is trained K times, each time with a different fold as the test set .
- Leave-One-Out Cross-Validation (LOO-CV): Each data point is used as a test case once, making K equal to the number of data points.
- Stratified K-Fold Cross-Validation: Ensures each fold has the same proportion of each class, typically used for imbalanced datasets.
Applications of Cross-Validation
- Model Evaluation : Provides an estimate of how the model will perform on unseen data.
- Hyperparameter Tuning : Helps in selecting the best model parameters by comparing performance across multiple folds.
- Prevention of Overfitting: Reduces the risk of overfitting by training on different subsets of data.
Example of Cross-Validation
In predicting housing prices, K-Fold Cross-Validation can be used to evaluate how well a regression model generalizes to different subsets of the housing dataset. Each fold helps assess the model’s performance on different training and testing sets.