What to do when my ML model is underfitted?

🙌 Let’s talk about what to do when your ML model is underfitted, in a super clear and simple way — to make it fun and easy to remember! 🤖📉📚

😕 What is Underfitting (Quick Reminder)?

Your model hasn’t learned enough from the training data — it’s too simple to capture the patterns.

📘 Like a student who didn’t study enough
❌ Fails both practice and final exams

🛠️ What To Do When Your Model is Underfitted?

Here’s your toolbox to fix it 🔧🧰👇

1. 🧠 Use a More Complex Model

Your model might be too basic!

✅ Add more:

• Layers or neurons (for neural networks) 🧱
• Depth or estimators (for decision trees, random forests) 🌲
• Features (inputs) that matter 🎯

🔁 Let it learn richer patterns!

2. 🏋️‍♂️ Train for More Epochs

Your model may need more time to learn!

⏳ If training and validation loss are still high, let it run longer.

📈 Keep training until it starts learning better!

3. 🎯 Reduce Regularization

Too much regularization = your model stays too simple 😓

✅ Try:

• Lower dropout rate (e.g., from 0.5 ➡️ 0.3)
• Reduce L1/L2 penalty strength (make lambda smaller)

Let your model have more freedom to learn! 🧠💡

4. 📈 Improve Feature Engineering

Sometimes, the model is okay — but the input data is not helpful.

✅ Try:

• Creating new features (ratios, averages, etc.)
• Removing noisy/irrelevant ones
• Using domain knowledge to add meaning 🌐

Better features = smarter learning! 🧮✨

5. 🔧 Tune Hyperparameters

Use a tool like Grid Search or Random Search to try different settings:

✅ Try different:

• Learning rates 🔢
• Activation functions (ReLU, Tanh, etc.) 🔌
• Optimizers (Adam, SGD, etc.) ⚙️

It’s like adjusting recipe ingredients for the perfect dish 🍲😋

6. 🔁 Try Different Architectures or Models

Sometimes switching models helps a lot!

✅ Example:

• Switch from linear regression ➡️ decision tree
• Or from shallow network ➡️ deep neural network

Try what fits your data best! 🧩

✅ Summary Table: