Evaluating the Accuracy of a Regression Model

To evaluate regression models, you first need to define some metrics. The common metrics used to evaluate regression models rely on the concepts of residuals and errors, which are quantifications of how much a model incorrectly predicts a particular data point. In the following sections, you will first learn about residuals and errors. You will then learn about two evaluation metrics, the MAE and RMSE, and how they are used to evaluate regression models.

Residuals and Errors

An important concept in understanding how to evaluate regression models is the residual. The residual refers to the difference between the value predicted by the model and the true value for a data point. It can be thought of as by how much your model missed a particular value. In the following diagram, we can see a best-fit (or regression) line with data points scattered above and below it. The distance between a data point and the line signifies how far away the...