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Classification and regression are two fundamental types of supervised learning tasks in machine learning, and they involve predicting different types of outcomes based on input data.
Objective: The goal of classification is to assign input data to predefined categories or classes.
Output: The output of a classification model is a discrete label or class. The classes are often mutually exclusive, meaning that an input data point is assigned to one and only one class.
Examples:
Algorithms:
Objective: The goal of regression is to predict a continuous numeric value.
Output: The output of a regression model is a numerical value that can fall within a range. Regression is used when the target variable is continuous and can take any value within a given range.
Examples:
Algorithms:
Nature of Output:
Task:
Output Space:
Evaluation Metrics:
Examples:
Algorithm Selection:
In summary, the primary distinction between classification and regression lies in the nature of the predicted output. Classification involves assigning data points to discrete categories, while regression involves predicting continuous numeric values. The choice between classification and regression depends on the nature of the problem and the type of output that best represents the task at hand.
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