Correct Answer: A

Overfitting refers to a model that has learned the training dataset too well, including the statistical noise or random fluctuations in the training dataset. Overfitting can have many causes and usually is a combination of "too powerful model" and "too many features". The more specialized the model becomes to training data, the less well it is able to generalize to new data, resulting in an increase in generalization error. Usually feature selection and engineering are employed to reduce the number of features to make the model more robust and useful.