Classification - Intro

• So far, we studied Regression, where the goal was to predict numbers (continuous values).
• Example: Predicting a student's exam score from their study hours.
• Now let’s move to Classification, where the goal is to predict categories (labels), not numbers.

Classification vs Regression

• Regression → Output is a number(e.g., price of a house, exam score, temperature).
• Classification → Output is a category (label)(e.g., spam vs. not spam, pass vs. fail, cat vs. dog).

Intuition

Think of classification as the task of drawing boundaries between different groups.

• In regression, we drew a line/curve to fit numbers.
• In classification, we draw a boundary (line/curve) to separate categories.

Regression

Classification

Logistic Regression - Intro

• So far, we used Regression to predict numbers (like exam scores or house prices).
• But what if the question is Yes/No instead of a number?