Cross-validation:

Cross-validation is a technique in machine learning where we train and test a model multiple times on different subsets of our dataset. It helps us get a more reliable measure of how well the model is likely to perform on new, unseen data.

Steps Involved in Cross-Validation:

  1. Data Splitting: Divide the dataset into training and testing sets.
  2. Model Training: Train the model on the training set.
  3. Model Testing: Test the model on the testing set.
  4. Performance Evaluation: Compute evaluation metrics (e.g., accuracy, precision, recall) for each iteration.
  5. Average Metrics: Average the evaluation metrics over all iterations to get a more reliable estimate of the model's performance.

 

Common Cross-Validation Techniques:

The choice of cross-validation technique depends on the specific dataset and task. Some popular methods include:

  1. K-fold cross-validation: The dataset is divided into k folds, and the model is trained and tested k times, each time using a different fold as the test set. A common choice is k=10, known as 10-fold cross-validation.
  2. Leave-one-out cross-validation (LOOCV): An extreme form of k-fold cross-validation, where each data instance serves as a separate fold. This method is computationally expensive but provides a very detailed assessment of the model.
  3. Stratified cross-validation: Ensures that each fold has approximately the same proportion of instances from each class, particularly useful for imbalanced datasets.