Training Set vs Validation Set vs Test Set

In machine learning, data is everything. However, just having data isn’t enough — how we divide it can make or break our model’s performance. This is where the concepts of training set, validation set, and test set come in.

The key difference is that training sets train the model, test sets evaluate it, and validation sets help optimize it.

Let’s dive into the purpose and differences between these three crucial subsets of data.

What is a training set?

The training set is the core dataset in the machine learning model development. It is the portion of our dataset that is used to train an algorithm to identify patterns, relationships, and structures within data. Think of it as the “learning material” for your model — the dataset it uses to build its internal understanding of how to make predictions or decisions.

During training, the model is fed input data (also called features) along with the correct output (also known as labels or targets). It then adjusts its internal parameters through a process called optimization, trying to minimize the error between its predicted outputs and the actual labels. This optimization is often performed using techniques like gradient descent in supervised learning.

Example:

Imagine you’re building a machine learning model to detect spam emails. Your training set might consist of 10,000 labeled emails, in which each email is marked as “spam” or “not spam.” The model analyzes patterns in the words, subject lines, and metadata of these emails to learn what typically characterizes a spam message.

Why is the training set important?

• It directly influences how well our model learns.
• A larger and more diverse training set usually leads to better performance because the model is exposed to more variations and edge cases.
• Poor quality or imbalanced training data can lead to biased or inaccurate models.

While the training set helps the model learn from labeled examples, it doesn’t give us insight into how well the model will generalize. That’s why the validation set is essential during model development.

What is a validation set?

The validation set is a separate subset of data used in the training phase of a machine learning model to evaluate and fine-tune its performance. While the training set helps the model learn, the validation set acts as a checkpoint — it tells us how well the model is generalizing data it hasn’t seen during training.

The validation set plays a big role in model tuning, especially when we’re adjusting hyperparameters (like learning rate, tree depth, number of layers, etc.) or comparing multiple models. It provides an unbiased evaluation that helps us make decisions without touching the test set, which should remain unseen until the final evaluation.

Example:

Continuing with the spam email classifier, suppose you trained your model on 10,000 emails. You could use a validation set of 2,000 different labeled emails to see if the model performs well on new messages. If the model does great on training data but poorly on the validation set, it’s likely overfitting — meaning it’s memorizing rather than learning.

Why is the validation set important?

• It helps in hyperparameter tuning without biasing the final model.
• It prevents overfitting by providing early stopping signals when performance starts degrading.
• It allows model comparison by giving a fair estimate of performance during development.

Though the validation set is used to improve the model during development, it still doesn’t give a definitive answer about how the model will perform on truly unseen data. That’s why we need the test set for the final evaluation.

What is a test set?

The test set is the final, untouched portion of the dataset, which is used to evaluate the performance of a fully trained and tuned machine learning model. Unlike the training and validation sets, which influence the model during development, the test set remains completely isolated until the very end. This isolation ensures that the model’s evaluation is unbiased and realistic — just like how it would perform in the real world.

The goal of using the test set is to get a true estimate of the model’s generalization ability — that is, how well it performs on data it has never seen or learned from.

Example:

Let’s go back to the spam email classifier. After training the model on 10,000 emails and tuning it using 2,000 validation emails, you now evaluate it on a separate test set of, say, 3,000 emails. Since the model has never seen these emails before, this test will reveal how accurately it classifies new, real-world messages as spam or not.

Why is the test set important?

• It provides an objective assessment of model performance.
• It helps ensure that our model can generalize to unseen data and is not overfit to the training/validation sets.
• It is used to compare multiple final models in terms of their real-world effectiveness.

Now that we’ve looked at the roles and responsibilities of each dataset, let’s compare the training set, validation set, and test set to understand their differences and how they work together in a typical machine learning pipeline.

Training set vs. validation set vs. test set

Here’s a side-by-side comparison between the training set, validation set, and test set:

Understanding when to use a training set vs test set is fundamental to machine learning success. The training set builds knowledge, while the test set validates real-world performance.

Next, let’s explore how to effectively split our dataset to include all three components.

How to split machine learning data

The standard practice is to divide the dataset into:

• 60% for training
• 20% for validation
• 20% for testing

However, this can vary depending on the dataset size and the complexity of the model. Larger datasets might allocate less to validation and testing, while smaller datasets might use techniques like cross-validation to make efficient use of limited data.

Here are a few tips for splitting data:

• Always shuffle your data before splitting to avoid bias.
• Use stratified sampling for classification tasks to maintain class balance across sets.
• Avoid any data leakage by ensuring that the test set remains unseen until the final evaluation.

With the data properly divided and roles clearly defined, you’re better prepared to build robust and reliable machine learning models.

Conclusion

Understanding training set vs validation set vs test set differences is essential for machine learning success. A well-structured workflow relies on three distinct datasets:

• The training set teaches the model.
• The validation set helps fine-tune and select the best model.
• The test set evaluates how the model performs on completely unseen data.

Proper data splitting not only boosts model performance but also ensures fairness and generalization. Mastering this fundamental step sets the stage for more accurate and trustworthy machine learning applications.

If you want to learn more about building a machine learning model, check out the Build a Machine Learning Model course on Codecademy.

Frequently asked questions

1. Why can’t I use the training set for testing the model?

You can’t use the training set for testing the model because the model has already seen the training data. Evaluating it on the same set would give an overly optimistic performance measure and may not reflect real-world results.

2. Can I skip the validation set?

Only if you’re not tuning hyperparameters, however, skipping it risks overfitting since there’s no intermediate check during training.

3. Is cross-validation better than using the validation set?

Cross-validation is often better for small datasets because it allows you to use all the data for both training and validation without overlap.

4. What happens if my test set is too small?

A small test set may not provide a reliable estimate of model performance and can lead to high variance in evaluation metrics.

5. Should I always keep the test set completely untouched?

Yes. To ensure a fair evaluation, the test set should remain hidden during both training and validation.