Overfitting is Like Overaccessorizing
Deep Learning is all about finding the perfect balance between complexity and generalizability. Overfitting, on the other hand, is when you've gone and overdone it.
Imagine your model is like a fancy new watch. You add a bunch of features, and at first, it's great! It tells time, date, temperature, and even predicts the weather. But then you start adding more features: a built-in coffee maker, a miniature fridge, and a tiny treadmill. Now you're just overaccessorizing.
Warning: Overfitting may lead to model madness. Prolonged exposure may cause:
- Model drift
- Feature bloat
- Training data hallucinations
- And a strong urge to add more widgets