Definition
How does your email know what spam looks like?
Supervised learning is machine learning from examples that come with the correct answers, called labels. The model learns to predict the label for new examples. It is the most widely used kind of machine learning.
In simple words
It is like learning with a teacher who marks every answer. Millions of people click "report spam", and each click is a label. The filter learns from those labelled emails and then sorts new emails by itself.
Why it matters
Spam filters, face unlock, price prediction, disease detection from scans and handwriting recognition all use supervised learning. Whenever you can collect examples with known answers, this is usually the first choice.
How it works
| Task | Example | Label |
|---|---|---|
| Spam filter | An email | Spam or not spam |
| Face unlock | A photo of a face | Owner or not owner |
| House price | Size, area, rooms | The price it sold for |
When the label is a category, such as spam or not spam, the task is called classification. When it is a number, such as a price, it is called regression.
Try it yourself
Learning with an answer key
Follow a teacher marking a student's work. Supervised learning works the same way.
Is this supervised learning?
Does each task learn from examples that come with the right answers?
Reading handwritten numbers using examples marked 0 to 9
Learning to spot spam from emails people marked as spam
Grouping customers by shopping habits with no labels
Predicting tomorrow's bus arrival from past times
A robot learning to walk from rewards for each step
Common mistakes
- Thinking supervised means a person watches the model all the time. It means the examples have labels.
- Forgetting that labels must be correct. Wrong labels teach wrong answers.
- Mixing up classification and regression.
Check yourself
Summary
- Supervised learning learns from labelled examples.
- Categories are classification; numbers are regression.
- It powers spam filters, face unlock and price prediction.
Finished reading?
Sources: Google Machine Learning Crash Course