Cross-Validation and Hyper-Parameter Tuning - Week 3

During the third week I was working on meta tools that for a given machine learning algorithm allow to extract meta information about it, e.g. predictions type, whether it takes a data::DatasetInfo parameter along with data and whether it supports weighted learning (#1031).

Now the developed tools can facilitate implementation of various cross validation strategies. I will start with a simple one - splitting data into training and validation sets in according with a given proportion.