Evaluating linear models

In this section, we will examine a number of metrics that we can use to evaluate the performance of a linear model other than using the MSE and cross-validation. We will look at some of the statistical tests and metrics that are used to evaluate how well a linear model performs, and to help decide between different linear model forms.

There are two statistical tests to be aware of for linear models, as follows:

  • First, is the test for whether one particular coefficient in the model is 0 or not. Failing to reject the null hypothesis indicates that the feature does not seem to contribute much to predictions. The following formulas show these hypotheses:
  • Second, is an overall test, that is, the F-test. This tests ...

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