Underachiever's Guide to Neural Network Underfitting

What is Underfitting, Anyway?

Underfitting is like being the kid who shows up to the party with a toaster for an appetizer. You might as well be trying, but it's just not enough.

Imagine a neural network with a brain the size of a peanut. That's underfitting.

Causes of Underfitting

• Too much data? No problem! Just add more data, and maybe a few more neurons.
• Insufficiently sized model? That's like trying to put a square peg in a round hole. It just won't fit.
• Model selection is like picking the right outfit for a job interview. You want to impress, but not too much.

Symptoms of Underfitting

• Model performs worse than a random guesser.
• Can't even beat a simple majority vote.
• Has a 50% chance of winning a game of chance.

Solutions to Underfitting

• Collect more data. Like, lots more.
• Increase model complexity. But not too much, or you'll just overfit.
• Use a bigger model. But don't say we didn't warn you.