Cross-validation part 1: N-Fold Partitioner

Before we can do cross validation, we need to partition the data into different sets of training and testing folds. In the standard leave-one-run-out cross-validation scheme we make N-partitions (for N-runs) where each run takes turns being the testing data, while the classifier is trained on all the other runs. This means that for every data fold we need a set of sample indices for training and another for testing. Below is a an incomplete function that computes the partitions for a given set of chunks sample attributes. Your task is to complete the function by writing the missing for-loop.

function partitions = cosmo_nfold_partitioner(chunks)

Hint: cosmo nfold partitioner skl

Solution: cosmo nfold partitioner

Extra exercise: write a split half partitioner where there are two partitions only of approximately equal size (for example, using odd and even chunks).

Hint: cosmo oddeven partitioner hdr

Solution: cosmo oddeven partitioner

Extra advanced exercise: write a (K,N)-fold partitioner that returns all partitions for N chunks so that there are K chunks in the test set and (N-K) chunks in the training set.