Can you explain the concepts of overfitting and underfitting?

Overfitting is a modeling error where the machine learning model learns “too much” from the training data, paying attention to the points of data that are noisy or irrelevant. Overfitting negatively impacts the models’ ability to generalize.

Underfitting is a scenario where a statistical model or the machine learning model cannot accurately capture the relationships between the input and output variables. Underfitting occurs because the model is too simple—informed by not enough training time, too few features, or too much regularization.