What is classification and regression in supervised Learning? Explain with example

In supervised learning, classification and regression are two primary types of tasks that involve predicting an output based on input data. Both of these tasks rely on labeled training data to learn the relationship between input features and targets, but they differ in the nature of the output variable they aim to predict. ### Classification **Definition:** Classification is a type of supervised learning where the goal is to predict a categorical label for a given input. The output variable is discrete and can take on a limited number of possible values, usually corresponding to different classes or categories. **Example:** Consider an email filtering system that aims to classify emails as either "spam" or "not spam." Here’s how it works: - **Input Features:** The email content, sender information, subject line, and other relevant textual features. - **Output Labels:** Two classes—"spam" and "not spam." In this case, the model would be trained on a dataset of emails labeled as either spam or not spam. After learning patterns from this labeled data, the model can evaluate new emails and classify them correctly based on the learned criteria. ### Regression **Definition:** Regression is another type of supervised learning where the objective is to predict a continuous numerical value based on input features. The output variable is continuous and can take on an infinite number of values. **Example:** An example of a regression task is predicting house prices based on various features of the house. Here’s how it works: - **Input Features:** Size of the house (in square feet), number of bedrooms, location, age of the property, etc. - **Output Variable:** The predicted price of the house (in dollars). In this scenario, the model is trained on historical data of houses sold, with each entry containing the features and the corresponding sale price. Once trained, the model can take input parameters about a new house and provide an estimated price based on the learned relationships. ### Summary Table | Aspect | Classification | Regression | |-------------------|-------------------------------------|-------------------------------------| | **Output Type** | Categorical (discrete classes) | Continuous (numerical values) | | **Goal** | Assign inputs to one of several classes | Predict a value based on input features | | **Examples** | Email spam detection, image recognition (dog vs. cat) | House price prediction, temperature forecasting | ### Conclusion Both classification and regression are fundamental tasks in supervised learning, used widely in various applications across domains such as finance, healthcare, marketing, and engineering. The choice between classification and regression typically depends on the nature of the output variable you are trying to predict.