Difference between Classification and Regression in Data Mining

In data mining, classification and regression are two commonly used techniques for predicting outcomes based on input data. Here are the main differences:

Classification:

• Classification is a supervised learning technique where the goal is to predict the categorical class labels of new data points.
• It is used when the target variable is discrete or categorical, such as classifying emails as spam or not spam.
• Common algorithms used for classification include Decision Trees, Support Vector Machines, and Logistic Regression.

Regression:

• Regression is also a supervised learning technique, but the goal is to predict continuous numerical values.
• It is used when the target variable is continuous, such as predicting house prices based on features like size and location.
• Common algorithms used for regression include Linear Regression, Polynomial Regression, and Random Forest.

In summary, classification is used for predicting categorical outcomes, while regression is used for predicting continuous numerical values.