Java: Data Science Made Easy

K-fold cross-validation

Holding out only one part of the data may not always be the best option. What we can do instead is splitting it into K parts and then testing the models only on 1/Kth of the data.

This is called k-fold cross-validation; it not only gives the performance estimation, but also the possible spread of the error. Typically, we are interested in models that give good and consistent performance. K-fold cross-validation helps us to select such models.

Next we prepare the data for k-fold cross-validation as follows:

• First, split the data into K parts
• Then, for each of these parts:
  • Take one part as the validation set
  • Take the remaining K-1 parts as the training set

If we translate this into Java, the first step will look ...