APPLICATION OF SUPERVISED LEARNING
NEURAL NETWORK
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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In a supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data. An unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own.
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supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data.
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unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own.
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All of Them
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Detailed explanation-1: -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.
Detailed explanation-2: -Supervised Learning predicts based on a class type. Unsupervised Learning discovers underlying patterns. And in Reinforcement Learning, the learning agent works as a reward and action system. Supervised learning maps labelled data to known output.
Detailed explanation-3: -Unsupervised learning techniques help the analyst identify data-driven patterns that may warrant further investigation. Supervised learning, on the other hand, includes techniques that require a defined response measure. Not surprisingly, analysts primarily use supervised learning techniques for predictive analytics.