COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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TRUE
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FALSE
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Either A or B
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None of the above
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Detailed explanation-1: -Supervised learning requires a labeled dataset for training. Unsupervised learning identifies hidden data patterns from an unlabeled dataset, while Reinforcement learning does not require data as it learns by interacting with the environment.
Detailed explanation-2: -Unsupervised learning is very much the opposite of supervised learning. It features no labels.
Detailed explanation-3: -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.
Detailed explanation-4: -Supervised learning maps labelled data to known output. Whereas, Unsupervised Learning explore patterns and predict the output. Reinforcement Learning follows a trial and error method. To sum up, in Supervised Learning, the goal is to generate formula based on input and output values.