What’s the Difference Between Regression and Classification Models?

Quality Thought: The Best Data Science Training Course Institute in Hyderabad with Live Internship

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What’s the Difference Between Regression and Classification Models?

Regression and classification are two fundamental types of predictive modeling in machine learning.

  • Regression models are used when the target variable is continuous. For example, predicting house prices, temperature, or sales revenue. The model outputs a numeric value based on input features.

  • Classification models are used when the target variable is categorical. For example, predicting whether an email is spam or not, or classifying an image as a cat or dog. The model assigns the input to one of the predefined categories.

In short, regression predicts how much, while classification predicts which category. Both are vital techniques in supervised learning, serving different kinds of problems.

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