Cross-validation is a resampling technique used in machine learning to assess the performance and generalizability of a model. K-Fold Cross-Validation is a specific form of cross-validation where the dataset is split into “K” subsets or folds, and the model is trained and evaluated “K” times, each time using a different fold as the test set and the remaining folds as the training set. This process helps ensure that the model’s performance is robust and not overly dependent on a particular subset of the data.
I. General Procedure:
1. Splitting the Data:
- The dataset is divided into K equally sized folds.
- Each fold is used exactly once as a validation while the K — 1 remaining folds form the training set.
2. Model Training and Evaluation:
- The model is trained K times, each time using a different fold as the validation set.
- The performance metric (e.g., accuracy, error) is calculated for each iteration.
3. Aggregating Results:
- The performance metrics from each iteration are averaged or otherwise combined to provide an overall assessment of the model’s…
