Chapter 2: Supervised Learning: Regression

This chapter introduces supervised learning for regression tasks. Regression aims to predict continuous numerical values, such as predicting house prices or temperature based on relevant features.

You will start with the fundamentals of linear regression, a foundational algorithm for this type of problem. We will cover how to:

Implement linear regression models using Scikit-learn's consistent API, specifically the LinearRegression estimator.
Interpret the coefficients learned by the model to understand feature importance.
Evaluate model performance using standard regression metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and the $R^2$ score.
Calculate these metrics effectively within Scikit-learn.

By the end of this chapter, you will build and evaluate a complete regression model using a practical dataset, applying the concepts learned.

Sections

2.1 Introduction to Regression Problems
2.2 Linear Regression Fundamentals
2.3 Implementing Linear Regression with Scikit-learn
2.4 Interpreting Model Coefficients
2.5 Regression Evaluation Metrics
2.6 Calculating Metrics in Scikit-learn
2.7 Hands-on Practical: Building a Regression Model