Randomness in Splitting

Splitting data into a training set and a test set is essential for getting a realistic idea of how a model will perform on new, unseen data. The training set is for learning, and the test set is for evaluation.

But how exactly do we perform this split? Should we just take the first 80% of our data rows for training and the remaining 20% for testing? Usually, that's not a good idea.

Consider why. Data often has some inherent order. Maybe it's sorted by the date it was collected, or perhaps it's grouped by category. If you have a dataset of customer sign-ups ordered by date, taking the first 80% for training and the last 20% for testing means your model learns only from older customers and is tested only on newer customers. Any trends or changes over time would make this evaluation unreliable. Similarly, if data for different categories in a classification problem is grouped together, a simple sequential split might result in some categories appearing only in the training set and others only in the test set.

The Importance of Shuffling

To prevent these problems and ensure both the training and test sets are representative of the overall data distribution, we need to introduce randomness. The standard practice is to shuffle the dataset randomly before splitting it.

Think of it like shuffling a deck of cards before dealing hands. Shuffling mixes up the original order, ensuring that when you split the data, both the training and test portions are likely to contain a similar mix of examples, patterns, and potential variations present in the overall dataset.

Let's visualize this. Imagine a small dataset for classifying shapes, initially ordered by shape type:

If we split this ordered data sequentially (e.g., 80/20), the training set gets all the circles and one square, while the test set gets only squares. The model won't learn effectively about squares, and the test results will be misleading.

Now, let's see what happens if we shuffle first:

After shuffling, the data order is mixed. Now, when we split, both the training and test sets are much more likely to contain a representative sample of both circles and squares. The test set now provides a better assessment of how the model generalizes.

Most machine learning libraries and tools that perform data splitting (like scikit-learn in Python) have shuffling enabled by default, precisely for this reason.

Reproducibility and Random State

While we want the shuffle to be random, we often need our experiments to be reproducible. If you split your data randomly today, and then run the same code tomorrow, you might get a slightly different random split, leading to slightly different evaluation results. This can make debugging or comparing approaches difficult.

To handle this, splitting functions usually allow you to set a random_state (sometimes called a seed). This is essentially a starting number for the random shuffling algorithm. If you use the same random_state value each time you run the split, you will get the exact same shuffled order and the exact same train/test split. This ensures that your results are consistent and can be reproduced by others (or by yourself later).

In summary, shuffling your data before splitting is a simple but fundamental step. It helps ensure that your training and test sets are unbiased samples of your overall data, leading to a more trustworthy evaluation of your model's ability to handle new, unseen examples.