COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Can be easier to search
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May avoid overfit since they are usually simpler (e.g. linear or low order decision surface)
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Both of the above
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None of the above
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Detailed explanation-1: -In machine learning, a hypothesis space is restricted so that these can fit well with the overall data that is actually required by the user. It checks the truth or falsity of observations or inputs and analyses them properly.
Detailed explanation-2: -The purpose of restricting hypothesis space in machine learning is so that these can fit well with the general data that is needed by the user.
Detailed explanation-3: -Hypothesis space is the set of all the possible legal hypothesis. This is the set from which the machine learning algorithm would determine the best possible (only one) which would best describe the target function or the outputs. Best Solution = Hypothesis. HYPOTHESIS.
Detailed explanation-4: -Hypothesis testing is done to confirm our observation about the population using sample data, within the desired error level. Through hypothesis testing, we can determine whether we have enough statistical evidence to conclude if the hypothesis about the population is true or not.
Detailed explanation-5: -Instance Space: It is a subset of all possible example or instance. Version Space: The Version Space denotes VSHD (with respect to hypothesis space H and training example D) is the subset of hypothesis from H consistent with training example in D. red: Generalization of Hypothesis.