Question: 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,

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.

Note:In this scenario, the goal is to find how many examples are required before the probability of finding a contradiction in the data reaches 0.5. As such, using an equation you must arrive at a numerical answer for this scenario.

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Problem Understanding In this scenario we have n Boolean attributes which means there are 2n possible examples The classification of examples is random meaning the target function is inconsistent The ... View full answer

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10. A hypothesis is a tentative, educated guess or proposition about the relationship

2. Develop a preliminary question from a topic or issue.

4. In the process of scientific discovery and explanation, four outcomes are sought: describing behavior, determining causes of behavior, predicting behavior, and explaining behavior.