Understanding Training, Dev (Validation), and Test Sets in Machine Learning

When developing a machine learning model, one fundamental principle is to separate the dataset into three distinct parts: training, dev (or validation), and test sets. This segregation is essential for creating robust models that generalize well to unseen data. But why do we need three different sets, and what is their purpose? This blog post aims to clarify these questions.

Training Set:

The training set is the portion of the dataset used to train the machine learning model. The model learns patterns and structures from this data, adjusting its weights and biases (in the case of neural networks) to minimize the difference between its predictions and the actual values. This process is often referred to as "learning" or "fitting the model to the data."

Dev (Validation) Set:

The dev set, also known as the validation set, is used to tune the model's hyperparameters and make decisions about the model's architecture. After training a model on the training set, we use the dev set to evaluate its performance. If the model's performance on the dev set is unsatisfactory, we adjust hyperparameters or make changes to the model's architecture, and then train the model again. This iterative process continues until we're satisfied with the model's performance on the dev set.

Test Set:

The test set is a portion of the dataset that we set aside and only use once, at the very end of our model-building process. Its purpose is to provide an unbiased evaluation of the final model, gauging how well the model generalizes to unseen data.

Why Do We Need a Dev Set?

There are two key reasons why we need a dev set when training neural networks or any machine learning model.

Overfitting: If we only had a training set and a test set, we would risk overfitting the model to the test data. Overfitting occurs when a model learns the training data so well that it does not generalize effectively to new, unseen data. By using a dev set, we can tune the model and make decisions about its architecture without touching the test set. This helps ensure that our final evaluation of the model's performance (using the test set) is unbiased.
Model Selection and Hyperparameter Tuning: The dev set allows us to compare different models (with different architectures or hyperparameters) and select the one that performs best. Without a dev set, we wouldn't have a reliable way to make these comparisons and decisions.