Steps involved in the K-fold Cross Validation in R

1. Split the data set into K subsets randomly
2. For each one of the developed subsets of data points
   • Treat that subset as the validation set
   • Use all the rest subsets for training purpose
   • Training of the model and evaluate it on the validation set or test set
   • Calculate prediction error
3. Repeat the above step K times i.e., until the model is not trained and tested on all subsets
4. Generate overall prediction error by taking the average of prediction errors in every case

To implement all the steps involved in the K-fold method, the R language has rich libraries and packages of inbuilt functions through which it becomes very easy to carry out the complete task. The following are the step-by-step procedure to implement the K-fold technique as a cross-validation method on Classification and Regression machine learning models.

The prime aim of any machine learning model is to predict the outcome of real-time data. To check whether the developed model is efficient enough to predict the outcome of an unseen data point, performance evaluation of the applied machine learning model becomes very necessary. K-fold cross-validation technique is basically a method of resampling the data set in order to evaluate a machine learning model. In this technique, the parameter K refers to the number of different subsets that the given data set is to be split into. Further, K-1 subsets are used to train the model and the left out subsets are used as a validation set.