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

Let’s break down what happens if your test and training datasets are the same size — clearly and to make it easy to understand for everyone! ✅📊🧠

📏 Can Test and Train Be the Same Size?

Yes, it’s totally okay if they are the same size — as long as they are made up of different data! 🙌

✅ Good Example (Same Size, Different Data)

Let’s say you have 10,000 samples.

You split them like this:

• 🧠 Training set: 5,000 samples
• 🎓 Test set: 5,000 samples

As long as the data in each set is unique (no overlap), you’re good! 👍

🚫 BAD Case: Same Data Used in Both

❗ If your train and test datasets contain the same samples (like copied), then:

💥 What Happens?

1. 🧠 Model just memorizes
   • Your model will “see” the answers during training.
   • It might look like it’s doing great 🎯, but it’s cheating 😅
2. 📉 You get fake performance
   • The test accuracy will be unrealistically high
   • But in real life, the model could fail on new data 🙈
3. ❌ No generalization
   • The model can’t handle data it hasn’t seen before.
   • This defeats the purpose of testing!

🎓 Why Do We Need a Test Set?

The test set is like the final exam. It should contain questions the model has never seen.

If it’s the same as training data, it’s like giving the answers ahead of time 📝➡️📘

✅ Summary