Artificial Intelligence Programming Practice Exam

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In terms of machine learning tasks, what is the key difference between classification and regression?

Classification predicts continuous values while regression predicts discrete labels

Classification predicts discrete labels while regression predicts continuous values

The distinction between classification and regression lies in the type of output they predict. In classification tasks, the model is designed to predict discrete labels or categories, meaning the target variable has specific, distinct classes. For instance, a classification algorithm might determine whether an email is "spam" or "not spam," categorizing instances into one of the limited classes.

On the other hand, regression tasks predict continuous values. This means that the output is a numerical value that can take on an infinite range within a certain interval. For example, regression might be used to predict the price of a house based on various features such as size, location, and number of bedrooms, where the output can be any value depending on the inputs.

Understanding this key difference helps clarify when to use classification models versus regression models based on the nature of the prediction problem at hand.

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Classification is based on clustering data, regression is based on pattern recognition

Classification involves supervised learning while regression involves unsupervised learning

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