In section 2, you will learn about the basic components of a regression problem, and we will discuss two approaches on how to solve them (linear and nonlinear methods). We will also discuss what a “good” regression model is, and how to evaluate regression models using two performance metrics: the MSE (mean squared error) and MAE (mean absolute error).

After this section you can:

• • Explain the concepts of a regression problem and explain two ways to solve it
  • Solve a KNN regression problem with pen and paper
  • Use two evaluation measures in regression and recognize their limitations
  • Explain the concept of an outlier and recognize whether they should be removed or not