how do you solve this problem: Suppose that a learning algorithm is trying to find a consistent hypothesis when the classifications of examples are actually random. There are n Boolean attributes, and examples are drawn uniformly from the set of $2$ to the power of n possible examples. Calculate the number of examples required before the probability of finding a contradiction in the data reaches $0\mathrm{.}5.$ Explain and solve ir