### Artificial IntelligenceAIMA Exercises

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 $2^n$ possible examples. Calculate the number of examples required before the probability of finding a contradiction in the data reaches 0.5.

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 $2^n$ possible examples. Calculate the number of examples required before the probability of finding a contradiction in the data reaches 0.5.

Submit Solution

Your Display Name
Email
Solution