Key Learning Points:

  • Validation data is used to check how well an AI model performs and whether it can handle various situations effectively.
  • When adjusting the settings of an AI model, validation data plays a key role in helping find the best balance.
  • The three steps—training, validation, and testing—each serve different purposes and are essential for building reliable AI models.

A Practice Test for AI? What Validation Data Really Is

When AI learns something, the process is somewhat similar to how we study for exams. For example, using textbooks to gain knowledge is like the “training” phase. Then, taking a practice test to check your understanding—that’s the “validation” phase. In this context, the practice test corresponds to what we call “validation data.”

Why Training Alone Isn’t Enough: The Role of Validation Data

In machine learning and deep learning, AI learns patterns and rules from large amounts of data. This data used for learning is called “training data.” However, if we only evaluate the model based on its performance with training data, we might run into a problem: it may work well in that specific situation but fail in others.

That’s where validation data comes in. This is a separate set of data not used during training. It helps us see whether the model can handle new or unfamiliar situations—in other words, whether it has generalization ability. Put simply, it checks whether the AI can deal with problems it hasn’t seen before.

Like Tasting While Cooking: How Validation Data Helps Tune Settings

Going a step further, validation data also plays an important role when fine-tuning an AI model’s settings. There are certain parameters—like “learning rate” or “batch size”—that aren’t decided by the AI itself. These are called “hyperparameters,” and humans need to adjust them through trial and error.

At this stage, validation data acts as a guide to help determine which settings seem promising. It’s like asking yourself during cooking, “Is this going in the right direction?” before finishing the dish.

This process is much like tasting your food while cooking a new recipe. Before serving it all at once, you take a small bite and think, “Maybe I should add a bit more salt.” That tasting step is exactly what validation data provides during model development.

But there’s something to be careful about here—if you keep tasting too much or adjust everything based only on that one bite, you might lose sight of what makes the dish truly good. Similarly, if you tailor your model too closely to the validation data alone, it may perform well on that specific set but poorly elsewhere. This issue is known as “overfitting.”

And just like how you’d want someone else to try your dish to know if it’s really good, in AI we use something called “test data” for that final judgment (we’ll cover this more in another article). In this way, training, validation, and testing each have their own roles—and all are necessary steps for building trustworthy AI.

An Essential Step Toward Building Trustworthy AI

Looking at it this way, even a single term like “validation data” involves thoughtful design and careful balancing. It’s not just about checking numbers—it plays a key role in finding that “just right” setting.

That’s why taking time with this step helps ensure that the overall AI model becomes more stable and reliable later on.

Although topics like these may seem technical at first glance, behind them lies a very human process of trial-and-error and intuitive judgment. More than simply aiming for correct answers, what matters is building up a process you can believe in. That mindset may be one of the most important elements in creating AI that continues to earn our trust moving forward.

Glossary

Validation Data: A set of data used to check whether an AI can apply what it has learned to new problems effectively.

Hyperparameters: Settings that influence how an AI model behaves; they cannot be automatically determined by the model itself and must be adjusted manually.

Overfitting: A state where an AI performs extremely well on specific data but poorly on anything else—losing flexibility or generalization ability.