A Shuffling Strategy for Partitioning

Let’s look at some subclasses that provide different partitioning strategies. We’ll start with one that shuffles and cuts, like a deck of cards.

Overview

One alternative is to shuffle and cut a list precisely the way a deck of cards is shuffled and cut before a game. We can use random.shuffle() to handle the randomized shuffling. The cut is – in a way – a hyperparameter. How large should the training set be compared to the testing set? Suggestions for knowledgeable data scientists include 80% to 20%, 67% to 30%, and an even 50% to 50% split. Because expert opinion varies, we need to provide a way for a scientist to adjust the partition ratio.

Implementation

We’ll make the split a feature of the class. We can create separate subclasses to implement alternative splits. Here’s a shuffling implementation: