{"id":50170,"date":"2024-04-15T23:06:27","date_gmt":"2024-04-15T23:06:27","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=50170"},"modified":"2024-04-15T23:06:27","modified_gmt":"2024-04-15T23:06:27","slug":"supervised-learning-differs-from-unsupervised-clustering-in-that-supervised-learning-requires","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/supervised-learning-differs-from-unsupervised-clustering-in-that-supervised-learning-requires\/","title":{"rendered":"Supervised learning differs from unsupervised clustering in that supervised learning requires"},"content":{"rendered":"

[amp_mcq option1=”at least one input attribute.” option2=”input attributes to be categorical.” option3=”at least one output attribute.” option4=”output attributes to be categorical.” correct=”option3″]<\/p>\n

The correct answer is: C. at least one output attribute.<\/p>\n

Supervised learning is a type of machine learning in which the model is trained on labeled data. This means that the model is given a set of data that includes both the input data and the desired output. The model then learns to map the input data to the output data.<\/p>\n

Unsupervised learning is a type of machine learning in which the model is trained on unlabeled data. This means that the model is given a set of data that does not include the desired output. The model then learns to find patterns in the data without any guidance.<\/p>\n

The main difference between supervised learning and unsupervised learning is that supervised learning requires a labeled dataset, while unsupervised learning does not.<\/p>\n

Option A is incorrect because supervised learning does not require all input attributes to be categorical. In fact, many supervised learning algorithms can work with both categorical and numerical input attributes.<\/p>\n

Option B is incorrect because supervised learning does not require input attributes to be categorical. In fact, many supervised learning algorithms can work with both categorical and numerical input attributes.<\/p>\n

Option D is incorrect because supervised learning does not require output attributes to be categorical. In fact, many supervised learning algorithms can work with both categorical and numerical output attributes.<\/p>\n","protected":false},"excerpt":{"rendered":"

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