Understanding the Role of the Validation Set in Machine Learning
When training a machine learning model, one of the most critical steps is ensuring that the model generalizes well to unseen data. To achieve this, we split our data into different subsets—commonly into training, validation, and test sets. While the training and test sets are widely understood, the validation set often gets overlooked or misunderstood. In this blog, we'll break down what a validation set is, why it matters, and how to use it effectively.
📦 What Is a Validation Set?
A validation set is a portion of your dataset that is not used to train the model, but instead is used during training to evaluate the model’s performance on unseen data. Think of it as a dress rehearsal before the final test.
When you train a model, it learns from the training set. But how do you know if it's learning the right patterns and not just memorizing the training data (i.e., overfitting)? That’s where the validation set comes in.
🔍 Why Do We Need a Validation Set?
Here are some key reasons for using a validation set:
-
Hyperparameter tuning: Algorithms like random forests or neural networks have hyperparameters (e.g., learning rate, number of layers). The validation set helps you test different settings and choose the best combination.
-
Model selection: You might try multiple models (e.g., logistic regression, SVM, XGBoost) and want to choose the one that performs best.
-
Early stopping: In deep learning, training can be stopped early if the performance on the validation set stops improving.
Without a validation set, you’d risk optimizing the model to perform well only on the training data, which defeats the purpose of machine learning.
📊 Typical Data Splits
Here’s how data is commonly split:
-
Training Set (60–80%): Used to fit the model.
-
Validation Set (10–20%): Used to tune hyperparameters and monitor performance during training.
-
Test Set (10–20%): Used only once—after all training and tuning—to evaluate final model performance.
In small datasets, people often use cross-validation (like k-fold CV) instead of a fixed validation set to make the most of limited data.
🧠 Example Workflow
Let’s say you're building a machine learning model to predict house prices.
-
Split the data:
-
70% for training
-
15% for validation
-
15% for testing
-
-
Train your model on the training data.
-
Tune hyperparameters (like learning rate, regularization strength) using the validation data.
-
Once you’ve selected the best model, evaluate it on the test set to report final metrics like accuracy or RMSE.
🚨 Common Pitfalls
-
Using the test set as validation: This leaks information and results in overly optimistic performance.
-
Tuning too much on the validation set: If you use the validation set too many times, it becomes a "soft test set" and can lead to overfitting on it as well.
To avoid this, some workflows use three splits (train/validation/test) or nested cross-validation.
✅ Conclusion
The validation set is a crucial tool in building robust machine learning models. It allows you to fine-tune and optimize your model while keeping the test set untouched for final evaluation. Ignoring it can lead to misleading results and poor generalization on real-world data.
So next time you're training a model, give your validation set the attention it deserves—it might be the difference between a model that just looks good and one that actually performs well.
