Understanding classification and regression is really important in supervised learning. These are the two main tasks we try to solve. Here’s why they are important:

1. Classification

• What It Means: This is about predicting a specific category or label. For example, figuring out if an email is "spam" or "not spam."

• Some Examples:

  • Recognizing images (like telling a cat apart from a dog).
  • Medical checks (deciding if a disease is present or not).

2. Regression

• What It Means: This is about predicting a number that can change. For instance, predicting prices or temperatures.

• Some Examples:

  • Guessing the price of a house based on things like its size and where it is located.
  • Predicting stock prices as time goes on.

Conclusion

Knowing the difference between classification and regression helps you pick the right tools and ways to measure success. This knowledge can help you make good choices when using models in real-life situations.