What to do when my ML model is Overfitted?

Awesome question! 😎 Let’s learn what to do when your ML model is overfitted — in a super clear and easy way, to help understand better! 🤖📉📊

😬 What is Overfitting (Quick Reminder)?

Your model is too good at remembering the training data but bad at handling new/unseen data.

📚 Trained too well on homework
❌ Fails on the test

🛠️ What To Do When Your Model is Overfitted?

Here are the top solutions — simple and powerful! 💪

1. ✂️ Use Less Complex Model

If your model is too big (too many layers/neurons/trees), it’s easy to overfit.

✅ Try a smaller neural network
✅ Reduce depth in decision trees or random forest

🧠 Simpler model = better generalization

2. 🧼 Add Regularization

This helps your model avoid memorizing too much.

✅ For neural networks:

• Dropout (randomly turns off neurons 🔌)
• L1 / L2 regularization (adds a penalty for large weights 🧮)

✅ For linear models:

• Ridge (L2) or Lasso (L1) regression

3. 🔄 Use More Data

More training data = better generalization! 📊📈

✅ Try to:

• Collect more data
• Use data augmentation (e.g., flipping, rotating images, paraphrasing text, etc.)

🆕 New examples help reduce overfitting!

4. 🧪 Use Early Stopping

Watch your validation loss 👀

📉 When validation loss starts going up, stop training!
✅ This saves your model from over-training

5. 📊 Cross-Validation

Instead of just one validation set, use k-fold cross-validation to check performance more fairly 💡

🔁 It splits your data into multiple sets and tests on each one

6. 🧠 Reduce Training Time

Too many epochs? Your model might memorize!

⏱️ Try reducing the number of training epochs

7. 🌪️ Add Noise to Data

This makes training harder and helps prevent memorizing.

✅ Add a bit of random noise to input data
✅ In text: change word order, add typos, etc.

✅ Summary Cheat Sheet: