Confident about Regularization? These 6 critical mistakes almost destroyed my dream ML project

Ever added regularization only to watch your model crash and burn? 🔥

Yeah, been there. Thought I was preventing overfitting, but instead, my model tanked.

Turns out, regularization isn’t a magic fix — it’s easy to mess up, leading to:
❌ Models that underfit and miss crucial patterns
❌ Overfitting disasters that fail in production
❌ Training so slow you could brew a coffee before each epoch ☕

After some trial and error (and maybe a few facepalms 🤦‍♂️), I figured out what was going wrong.

Here are six regularization mistakes that could be sabotaging your model — and how to fix them!

1: Over-regularizing until the model becomes useless

What happens?

Too much regularization shrinks model weights into oblivion, making it too simple.
The result? It misses key patterns and underfits the data.

Signs you’re over-regularizing:

🚨 Low accuracy across training, validation, and test sets
🚨 The model struggles with both bias and…