Overfitting and underfitting are important concepts to understand in the context of machine learning. These concepts relate to the ability of a model to generalize well beyond the training data. Let's start by defining each term:

Overfitting: Overfitting occurs when a model learns the specific patterns and noise in the training data too well, to the extent that it performs poorly on unseen data. In other words, the model becomes too complex and adapts too closely to the training set, losing its ability to generalize.

Underfitting: On the other hand, underfitting happens when a model is too simple and fails to capture the underlying patterns in the data. This leads to poor predictive performance both on the training data and the unseen data.

Understanding overfitting and underfitting is crucial because it helps in creating models that strike the right balance, allowing for accurate predictions on new, unseen data.