APPLICATION OF SUPERVISED LEARNING
MACHINE LEARNINGHARD QUESTIONS
Question
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Probably Approximate Cost


Probably Approximate Correct.


Probably Approximate Communication


Probably Approximate Computation PAC stands for Probably Approximate Correct.

Detailed explanation1: In computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning.
Detailed explanation2: Approximately correct means the interval is close enough to the true interval that the error will be small on new samples, and Probably means that if we play the game over and over we’ll usually be able to get a good approximation. That is, we’ll find an approximately good interval with high probability.
Detailed explanation3: The basic idea of the PAC model is to assume that examples are being provided from a fixed (but perhaps unknown) distribution over the instance space. The assumption of a fixed distribution gives us hope that what we learn based on some training data will carry over to new test data we haven’t seen yet.
Detailed explanation4: Probably approximately correct (PAC) learning is a theoretical framework for analyzing the generalization error of a learning algorithm in terms of its error on a training set and some measure of complexity. The goal is typically to show that an algorithm achieves low generalization error with high probability.