Classification and Regression
Two Major Problem Types
Classification
Classification means predicting a category, such as spam or not spam, cat or dog, or positive or negative. The output is usually a label or a probability for each class.
Regression
Regression means predicting a continuous number, such as house price, temperature, or sales volume. The output is a numeric value rather than a class label.
Classification vs Regression
Classification
- Predicts categories
- Often uses class probabilities
- Examples: email spam detection, image labeling
Regression
- Predicts numbers
- Often uses a single numeric output
- Examples: price prediction, demand forecasting
Match the Task to the Output
Choose classification when the answer is a class, and regression when the answer is a number.
Which task is classification?
Spam detection predicts a category.
Correct answer: Predicting whether an email is spam
Give one example of a regression problem.
Regression predicts continuous numeric values.
Correct answer: Predicting house price.
Which output best matches regression?
Regression produces a numeric value.
Correct answer: A continuous number