Assessing Model Performance for Regression Models
When you create a regression model, you create a model that predicts a continuous numerical variable, as you learned in Chapter 2, Regression. When you set aside your evaluation dataset, you have something that you can use to compare the quality of your model.
What you need to do to assess your model quality is compare the quality of your prediction to what is called the ground truth, which is the actual observed value that you are trying to predict. Take a look at Figure 6.4, in which the first column contains the ground truth (called actuals) and the second column contains the predicted values:
0 in the output compares the actual value in our evaluation dataset to what our model predicted. The actual value from our evaluation dataset is
4.891. The value that the model predicted is
1 compares the actual value of
4.194 to what the model predicted...