What happens if the validation and test datasets are the same size?

Great question again! 🙌 Let’s explain what happens if the validation and test datasets are the same size — in a simple way

⚖️ Can Validation and Test Sets Be the Same Size?

Yes, they can be the same size — but it’s not about size, it’s about purpose 🎯

So, even if they have the same number of samples, their roles are very different:

🧪 Validation Set – What’s It For?

• Used during training
• Helps you tune the model and make choices (like stopping early or changing learning rate)
• It’s like a practice test 📝

🎓 Test Set – What’s It For?

• Used after training is completely done ✅
• It gives you the final score
• No changes should be made based on test results ❌🔧
• It’s like the final exam 🎓

🤔 What If They’re the Same Size?

That’s totally fine! ✅

Let’s say you split your data like this:

• 60% ➡️ Training 🧠
• 20% ➡️ Validation 🧪
• 20% ➡️ Test 🎓

Here, validation and test are equal in size, and that’s perfectly okay! 👌

🚫 What You Should NOT Do

Here’s the danger ❗

• Don’t use the same dataset for both validation and test.
  ➤ That would give you a false sense of performance 😬
  ➤ Your model would “peek” at the answers!

📛 Same data ➕ used for both validation & test = ❌ Bad idea

🧠 Summary