What happen if my train and validation dataset are same size?

Great! 😊 Let’s talk about what happens if your training and validation datasets are the same size — in a clear, and simple way

✅ Can Train and Validation Be the Same Size?

Yes, absolutely! 💯

It’s totally okay if your training set and validation set have the same number of samples — as long as they are made of different data 🟰📁📁

🎯 Example:

Let’s say you have 10,000 total samples:

• 🧠 Training Set = 5,000 samples
• 🧪 Validation Set = 5,000 samples

➡️ Same size? ✅ No problem
➡️ Same data? ❌ Big problem

🔍 What Really Matters?

✅ Data in training and validation sets should be different.
They must not overlap — or else your model will get confused and your validation results will lie 😵‍💫

🧠 Why Validation Size Matters Less Than Separation

• Purpose of validation: to check how well your model is learning on unseen data 📉
• So, the amount of data isn’t as important as having fresh, clean data it hasn’t seen

🚫 What You Shouldn’t Do

❌ Using the same data for both training and validation

• It’s like testing yourself using the same questions you studied 📝➕📘
• Your model might look great 😎 but fail on real-world data 😢

📏 How to Choose Sizes? (General Rule)

A typical data split looks like this:

• 80% training 🧠
• 10%–20% validation 🧪
• 10% test 🎓

But if your dataset is small, 50/50 (train/val) is okay too! Just keep the data separate 🔀

✅ Summary