Regression Vs Classification

Regression

Regression is used to predict outputs that are continuous. The outputs are quantities that can be flexibly determined based on the inputs of the model rather than being confined to a set of possible labels. 


Classification

Classification is used to predict a discrete label. The outputs fall under a finite set of possible outcomes. Many situations have only two possible outcomes. This is called binary classification (True/False, 0 or 1).

Multi-label classification is when there are multiple possible outcomes. It is useful for customer segmentation, image categorization, and sentiment analysis for understanding text. To perform these classifications, we use models like Naive Bayes, K-Nearest Neighbors, and SVMs.

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