How cross-validation works? What is K-fold cross-validation?
Cross-validation is the process to separate your total training set into two subsets: training and validation set, and evaluate your model to choose the hyperparameters. But you do this process iteratively, selecting differents training and validation set, in order to reduce the bias that you would have by selecting only one validation set.
K fold cross validation is a method of cross validation where we select a hyperparameter k. The dataset is now divided into k parts. Now, we take the 1st part as validation set and remaining k-1 as training set. Then we take the 2nd part as validation set and remaining k-1 parts as training set. Like this, each part is used as validation set once and the remaining k-1 parts are taken together and used as training set. It should not be used in a time series data.
How do we choose K in K-fold cross-validation? What’s your favorite K?
There are two things to consider while deciding K: the number of models we get and the size of validation set. We do not want the number of models to be too less, like 2 or 3. At least 4 models give a less biased decision on the metrics. On the other hand, we would want the dataset to be at least 20-25% of the entire data. So that at least a ratio of 3:1 between training and validation set is maintained.
I tend to use 4 for small datasets and 5 for large ones as K.