COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Supervised Learning
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Unsupervised Learning
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Either A or B
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None of the above
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Detailed explanation-1: -The goal of feature selection for unsupervised learning is to find the smallest feature subset that best uncovers “interesting natural” groupings (clusters) from data accord-ing to the chosen criterion. There may exist multiple redundant feature subset solutions. We are satisfied in finding any one of these solutions.
Detailed explanation-2: -The main goal of unsupervised learning is to discover hidden and interesting patterns in unlabeled data. Unlike supervised learning, unsupervised learning methods cannot be directly applied to a regression or a classification problem as one has no idea what the values for the output might be.
Detailed explanation-3: -Commonly referred to as unsupervised machine learning, analyzes and groups unlabeled datasets using machine learning algorithms. These algorithms identify hidden patterns or data clusters without human assistance.
Detailed explanation-4: -The main difference between supervised and unsupervised learning: Labeled data. The main distinction between the two approaches is the use of labeled datasets. To put it simply, supervised learning uses labeled input and output data, while an unsupervised learning algorithm does not.