DATABASE FUNDAMENTALS
DATA WAREHOUSING AND DATA MINING
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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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Reinforcement learning
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Machine learning
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Detailed explanation-1: -In Data mining, the problem of unsupervised learning is that of trying to find hidden structure in unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution.
Detailed explanation-2: -Unsupervised learning uses machine learning algorithms to analyze and cluster unlabeled data sets. These algorithms discover hidden patterns in data without the need for human intervention (hence, they are “unsupervised”).
Detailed explanation-3: -Explanation: Unsupervised learning is a type of machine learning algorithm that is specifically designed to identify the abstracted patterns in unlabeled data.
Detailed explanation-4: -Unsupervised learning uses unlabeled training samples to model basic characteristics of an ML system’s input data. These characteristics can be a useful starting point for supervised learning, and they can be used to extrapolate what is learned from labeled training data.
Detailed explanation-5: -In supervised learning, input data is provided to the model along with the output. In unsupervised learning, only input data is provided to the model. The goal of supervised learning is to train the model so that it can predict the output when it is given new data.