What is PAC Learning explain with example?

Summary. 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 …

What is meant by PAC learning?

In computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant.

What makes a hypothesis set PAC learnable?

To be PAC Learnable, there must be a hypothesis h ∈ H with arbitrary small error for every f ∈ C. We generally assume H is a super set of C. The worst definition is that the algorithm must meet its accuracy for every distribution and every target function f ∈ C. Proof Let h be a bad hypothesis.

Is Pac learning important?

Probably approximately correct (PAC) learning theory helps analyze whether and under what conditions a learner L will probably output an approximately correct classifier.

How do you prove PAC Learnability?

If the concept class is finite, m needed to obtain a PAC hypothesis is polynomi- ally bounded in 1/δ, 1/ϵ, and log |C|. So if C is not extremely large, it is PAC learnable. For instance, if C is all conjunctions of n Boolean variables, then log |C| = log 3n = O(n) so it is PAC learnable.

What is Epsilon in Pac learning?

Probability[error(h) > epsilon] < delta. We are now in a position to say when a learned concept is good: When the probability that its error is greater than the accuracy epsilon is less than the confidence delta.

What is not PAC learnable?

A hypothesis class is not PAC learnable if it has infinite VC dimension, for example the class of polynomial classifiers over R , or the class of unions of intervals H = ∪ki=1[ai,bi]|k∈Rai≤bi.