APPLICATION OF SUPERVISED LEARNING
NEURAL NETWORK
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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No Free Lunch Theorem for Machine Learning (Wolpert, 1996) states that
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No ML algorithm can solve unsolved problems
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No ML algorithm can can be trained without supervision
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No ML algorithm is better than any other across all possible datasets/problems
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The more types of problems you want to solve the more work you need to put into your ML algorithm
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It is impossible for ML algorithms to solve certain problems
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Explanation:
Detailed explanation-1: -The no-free-lunch theorem of optimization (NFLT) is an impossibility theorem telling us that a general-purpose, universal optimization strategy is impossible. The only way one strategy can outperform another is if it is specialized to the structure of the specific problem under consideration.
Detailed explanation-2: -The No Free Lunch theorems prove that under a uniform distribution over induction problems (search problems or learning problems), all induction algorithms perform equally.
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