Disadvantages of K-fold Cross-Validation
- A lower value of K leads to a biased model and a higher value of K can lead to variability in the performance metrics of the model. Thus, it is very important to use the correct value of K for the model (generally K = 5 and K = 10 is desirable).
K-fold Cross Validation in R Programming
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.