In classification tasks, it is easy to construct a confusion matrix, which shows many samples were classified correctly (true and false positives), and how many samples were classified incorrectly (true and false negatives). The various metrics that can be computed from the confusion matrix are quite easy to understand.
What if my target variable is continuous (e.g. if I am predicting the height of a person based on some genetic, environmental, etc. data)? What will be my "chance level accuracy"? How do I analyze the correctness of the classifier?