Overfitting and Underfitting

Overfitting memorises the training data. Underfitting ignores it. Both fail on new data.

Definition

Overfitting is when a model learns the training data too specifically, including noise, and generalises poorly. Underfitting is when a model is too simple to capture the real patterns in the data.

Step 1

Predict first

In one or two sentences, what do you think this concept means? Wrong is fine; the small struggle is what makes the answer stick.