MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Any hypothesis found to approximate the target function well over a sufficiently large set of training examples will also approximate the target function well over other unobserved examples.

Detailed explanation-2: -The CANDIDATE-ELIMINATION algorithm represents the set of all hypotheses consistent with the observed training examples.

Detailed explanation-3: -HYPOTHESIS SPACE. 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: -In a concept learning task, a human classifies objects by being shown a set of example objects along with their class labels. The learner simplifies what has been observed by condensing it in the form of an example. This simplified version of what has been learned is then applied to future examples.

Detailed explanation-5: -A hypothesis is an explanation for something. It is a provisional idea, an educated guess that requires some evaluation. A good hypothesis is testable; it can be either true or false. In science, a hypothesis must be falsifiable, meaning that there exists a test whose outcome could mean that the hypothesis is not true.