Question 1

What is overfitting, and how do you detect it?

Accepted Answer

Overfitting happens when a model learns the noise in its training data, so it scores well on training data but poorly on new data. Detect it by comparing training and validation scores. A large gap, with high training accuracy and lower validation accuracy, points to overfitting. Fix it with more data, a simpler model, regularisation, or early stopping.

Question 2

Why do we split data into training and test sets?

Accepted Answer

The test set is held back during training, so it stands in for new data the model has never seen. Its score gives an honest estimate of how the model will perform in practice. If you tested on the same data you trained on, the score would look better than reality, because the model may have memorised it. Cross-validation extends this idea by rotating which part of the data is held out.

Machine Learning