Cross-Validation and Hyper-Parameter Tuning - Week 11

Last week I was primarilly finishing working on k-fold cross-validation and the main part of the hyper-parameter tuning module. Sending a PR for k-fold cross-validation to the main repository was delayed since there was a bug in the linear regression copy constructor which caused failing for one of k-fold cross-validation tests. Now it is fixed, and a PR for k-fold cross-validation has been sent.

As I have already mentioned, I was also finishing working on the main part of the hyper-parameter tuning module. Mainly it concerns using a new interface for mlpack optimizers that is about accepting a DatasetMapper parameter describing data type and possible values (if it is of categorical type) for each dimension (that corresponds to a hyper-parameter in the case of hyper-parameter optimization).

During the remaining time of GSoC I'm going to work on supporting gradient decent for hyper-parameter tuning, as well as to write a final report.