Performance measures are used to evaluate learning algorithms and form an important aspect of machine learning. In some cases, these measures are also used as heuristics to build learning models.
Now let's explore the concept of the Probably Approximately Correct (PAC) theory. While we describe the accuracy of hypothesis, we usually talk about two types of uncertainties as per the PAC theory:
The following graph shows how the number of samples grow with error, probability, and hypothesis:
The error measures for a classification and prediction ...