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Cross-validation is a technique used to evaluate the performance of a machine learning model. It helps in tuning model parameters by estimating how well the model will perform on unseen data. The process involves dividing the dataset into multiple parts, training the model on some parts, and testing it on others. This article explains the step-by-step calculation of the cross-validation error during model tuning.
Step 1: Data Partitioning
The dataset is divided into ‘k’ equal parts, called folds. Common choices for ‘k’ are 5 or 10. Each fold acts as a validation set once, while the remaining folds form the training set. This process ensures that every data point is used for both training and validation.
Step 2: Model Training and Validation
For each fold, the model is trained on the remaining ‘k-1’ folds. It is then validated on the current fold. The error is calculated based on the model’s predictions compared to the actual values in the validation fold. This step is repeated for all folds.
Step 3: Error Calculation
The errors from each fold are recorded. Common error metrics include mean squared error (MSE) or mean absolute error (MAE). The cross-validation error is the average of these errors across all folds, providing an estimate of the model’s performance.
Step 4: Final Error Estimation
The average error obtained from the cross-validation process serves as an estimate of how the model will perform on new, unseen data. This value guides the selection of optimal model parameters during tuning.