W przypadku, gdy systemy są w stanie wykonywać się w sposób niezależny, należy wprowadzić w sposób niezależny od wymogów określonych w pkt 1.

Cross- validation is a technique used to evaluate thee performance of a machine learning model. It helps in tuning model parameters by estimating how well thee model perfor on unseen data. The process involves dividing the dataset into multiple parts, cooring the model omen some parts, and testing it on ots. This article explains thes step calculation of thee cross- validation error during model tuning tuning.

Krok 1: Data Partitioning

Te dane is divided into; k is; equal parts, called folds. Common choices for; k cor; are 5 or 10. Each fold acts a validation set once, while te equading folds form thee training set. Thi process ensures that every data point i s used for both training and validation.

Step 2: Model Training andValidation

For each fold, thee model is stacjonuje on thee resideng; k- 1 edits; folds. It s then validated on thee current fold. The error is calculated based one thee model 's predications compared to thee actual values in thee validation fold. This step is repeated for all folds.

Krok 3: Error Calculation

Te błędy w porównaniu z tym, co się stało, to nie było to możliwe.

Krok 4: Final Error Estimation

Te average error portained from the cross- validation process serves an estimate of how the model perfom on new, unseen data. Thii value guides the selection of optimal model parameters during tuning.