we discussed about cross validation and k fold

In k-fold cross-validation, the dataset is divided into ‘k’ subsets. For each iteration, the model is trained on ‘k-1’ subsets and tested on the remaining one. This is repeated ‘k’ times, each time with a different subset as the test set. For example, in a 5-fold cross-validation, the data is partitioned into 5 parts, training on 4 and testing on the fifth. This cycle is done 5 times. The model’s performance is then averaged across all rounds, giving a more comprehensive assessment of its generalization ability