Overfitting and Underfitting Explained

A machine learning model is only useful if it performs well on data it has never seen before, not just the data it was trained on.

Overfitting happens when a model learns the training data too closely, including its noise and random details, instead of the actual underlying pattern.

• Training accuracy stays very high, since the model has essentially memorized the data.
• Test accuracy drops noticeably, since the model fails to generalize to new inputs.

Underfitting happens when a model is too simple to capture the real pattern in the data.

• Training accuracy stays low, since the model has not learned enough.
• Test accuracy also stays low because the model never learned a useful pattern in the first place.