What is the difference between Regression and Classification?

The difference between Regression and Classification comes down to what kind of output you want to predict.

Core Difference

• Regression → predicts a continuous value (numbers)
• Classification → predicts a category or class (labels)

Regression (Continuous Output)

Regression is used when the output is a number with a range.

Examples:

• Predicting house price → ₹25,00,000
• Predicting temperature → 32.5°C
• Predicting salary → ₹8.7 LPA

Common Algorithms:

• Linear Regression
• Polynomial Regression

Classification (Categorical Output)

Classification is used when the output is a label or category.

Examples:

• Email → Spam / Not Spam
• Disease → Positive / Negative
• Image → Cat / Dog

Common Algorithms:

• Logistic Regression
• Decision Trees
• Random Forest

Key Differences Table

Simple Analogy

• Regression = “Kitna?” (How much?)
• Classification = “Kaunsa type?” (Which category?)

In One Line

Regression predicts numbers, while classification predicts categories.