Cross-validation is a technique used to evaluate thon performance of a machine learning model. It helps in tuning model parametrs by estimating how well thee model will will perfom on unseen data. Te process enterpeves discling thate dataset into multiple parts, traing thee model on some parts, and testing it on other tuning. This article exestaines te step- by- step calculation of thee cross - validation error during model tuning. This article compliains e ster moll.

Step 1: Data Partitioning

Te dataset is divided into ato application set once, while te estaing folds form the traing set. This process ensures that every data point is used for both traing and validation.

Step 2: Model Training and Validation

For each fold, thee model is trained on then beging contraing; k-1 contrads; folds. It is then validated on thon thee current fold. Thee error is calculated based on thee model 's predictions compared to e actual values in thee validation fold. This step is repecated for all folds.

Step 3: Error Calculation

Ty errors from each fold are applided. Common error metrics include meade mean squared error (MSE) or mear mean absolute error (MAE). Thee cross-validation error is te average of these errors across all folds, proving an estimate of thee model 's execurance.

Step 4: Final Error Estimation

Te average error dosažen from the cross-validation process serves as an estimate of how the model wil perforem on new, unseen data. This value guides thee selektion of optimal model parametrs during tuning.