Datasets: Dividing the original dataset

  • Machine learning models should be tested against a separate dataset, called the test set, to ensure accurate predictions on unseen data.

  • It's recommended to split the dataset into three subsets: training, validation, and test sets, with the validation set used for initial testing during training and the test set used for final evaluation.

  • The validation and test sets can "wear out" with repeated use, requiring fresh data to maintain reliable evaluation results.

  • A good test set is statistically significant, representative of the dataset and real-world data, and contains no duplicates from the training set.

  • It's crucial to address discrepancies between the dataset used for training and testing and the real-world data the model will encounter to achieve satisfactory real-world performance.

All good software engineering projects devote considerable energy to testing their apps. Similarly, we strongly recommend testing your ML model to determine the correctness of its predictions.

Training, validation, and test sets

You should test a model against a different set of examples than those used to train the model. As you'll learn a little later, testing on different examples is stronger proof of your model's fitness than testing on the same set of examples. Where do you get those different examples? Traditionally in machine learning, you get those different examples by splitting the original dataset. You might assume, therefore, that you should split the original dataset into two subsets:

Figure 8. A horizontal bar divided into two pieces: ~80% of which is the training set and ~20% is the test set.
Figure 8. Not an optimal split.

 

Exercise: Check your intuition

Suppose you train on the training set and evaluate on the test set over multiple rounds. In each round, you use the test set results to guide how to update hyperparameters and the feature set. Can you see anything wrong with this approach? Pick only one answer.
Doing many rounds of this procedure might cause the model to implicitly fit the peculiarities of the test set.
Yes! The more often you use the same test set, the more likely the model closely fits the test set. Like a teacher "teaching to the test," the model inadvertently fits the test set, which might make it harder for the model to fit real-world data.
This approach is fine. After all, you're training on the training set and evaluating on a separate test set.
Actually, there's a subtle issue here. Think about what might gradually go wrong.
This approach is computationally inefficient. Don't change hyperparameters or feature sets after each round of testing.
Frequent testing is expensive but critical. However, frequent testing is far less expensive than additional training. Optimizing hyperparameters and the feature set can dramatically improve model quality, so always budget time and computational resources to work on these.

Dividing the dataset into two sets is a decent idea, but a better approach is to divide the dataset into three subsets. In addition to the training set and the test set, the third subset is:

  • A validation set performs the initial testing on the model as it is being trained.
Figure 9. A horizontal bar divided into three pieces: 70% of which is the training set, 15% the validation set, and 15% the test set
Figure 9. A much better split.

Use the validation set to evaluate results from the training set. After repeated use of the validation set suggests that your model is making good predictions, use the test set to double-check your model.

The following figure suggests this workflow. In the figure, "Tweak model" means adjusting anything about the model —from changing the learning rate, to adding or removing features, to designing a completely new model from scratch.

Figure 10. A workflow diagram consisting of the following stages: 1. Train model on the training set. 2. Evaluate model on the validation set. 3. Tweak model according to results on the validation set. 4. Iterate on 1, 2, and 3, ultimately picking the model that does best on the validation set. 5. Confirm the results on the test set.
Figure 10. A good workflow for development and testing.

The workflow shown in Figure 10 is optimal, but even with that workflow, test sets and validation sets still "wear out" with repeated use. That is, the more you use the same data to make decisions about hyperparameter settings or other model improvements, the less confidence that the model will make good predictions on new data. For this reason, it's a good idea to collect more data to "refresh" the test set and validation set. Starting anew is a great reset.

Exercise: Check your intuition

You shuffled all the examples in the dataset and divided the shuffled examples into training, validation, and test sets. However, the loss value on your test set is so staggeringly low that you suspect a mistake. What might have gone wrong?
Many of the examples in the test set are duplicates of examples in the training set.
Yes. This can be a problem in a dataset with a lot of redundant examples. We strongly recommend deleting duplicate examples from the test set before testing.
Training and testing are nondeterministic. Sometimes, by chance, your test loss is incredibly low. Rerun the test to confirm the result.
Although loss does vary a little on each run, it shouldn't vary so much that you think you won the machine learning lottery.
By chance, the test set just happened to contain examples that the model performed well on.
The examples were well shuffled, so this is extremely unlikely.

Additional problems with test sets

As the previous question illustrates, duplicate examples can affect model evaluation. After splitting a dataset into training, validation, and test sets, delete any examples in the validation set or test set that are duplicates of examples in the training set. The only fair test of a model is against new examples, not duplicates.

For example, consider a model that predicts whether an email is spam, using the subject line, email body, and sender's email address as features. Suppose you divide the data into training and test sets, with an 80-20 split. After training, the model achieves 99% precision on both the training set and the test set. You'd probably expect a lower precision on the test set, so you take another look at the data and discover that many of the examples in the test set are duplicates of examples in the training set. The problem is that you neglected to scrub duplicate entries for the same spam email from your input database before splitting the data. You've inadvertently trained on some of your test data.

In summary, a good test set or validation set meets all of the following criteria:

  • Large enough to yield statistically significant testing results.
  • Representative of the dataset as a whole. In other words, don't pick a test set with different characteristics than the training set.
  • Representative of the real-world data that the model will encounter as part of its business purpose.
  • Zero examples duplicated in the training set.

Exercises: Check your understanding

Given a single dataset with a fixed number of examples, which of the following statements is true?
Every example used in testing the model is one less example used in training the model.
Dividing examples into train/test/validation sets is a zero-sum game. This is the central trade-off.
The number of examples in the test set must be greater than the number of examples in the validation set.
In theory, the validation set and test test should contain the same number of examples or nearly so.
The number of examples in the test set must be greater than the number of examples in the validation set or training set.
The number of examples in the training set is usually greater than the number of examples in the validation set or test set; however, there are no percentage requirements for the different sets.
Suppose your test set contains enough examples to perform a statistically significant test. Furthermore, testing against the test set yields low loss. However, the model performed poorly in the real world. What should you do?
Determine how the original dataset differs from real-life data.
Yes. Even the best datasets are only a snapshot of real-life data; the underlying ground truth tends to change over time. Although your test set matched your training set well enough to suggest good model quality, your dataset probably doesn't adequately match real-world data. You might have to retrain and retest against a new dataset.
Retest on the same test set. The test results might have been an anomaly.
Although retesting might yield slightly different results, this tactic probably isn't very helpful.
How many examples should the test set contain?
Enough examples to yield a statistically significant test.
Yes. How many examples is that? You'll need to experiment.
At least 15% of the original dataset.
15% may or may not be enough examples.
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