Evaluating the linear regression model

In this recipe, we'll look at how well our regression fits the underlying data. We fitted a regression in the last recipe, but didn't pay much attention to how well we actually did it. The first question after we fitted the model was clearly, how well does the model fit? In this recipe, we'll examine this question.

Getting ready

Let's use the lr object and Boston dataset—reach back into your code from the Fitting a line through data recipe. The lr object will have a lot of useful methods now that the model has been fit.

How to do it...

1. Start within IPython with several imports, including numpy, pandas, and matplotlib for visualization: 

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

%matplotlib inline

2. It is worth looking at a Q-Q plot. We'll use scipy here because it has a built-in probability plot:

from scipy.stats import probplot
f = plt.figure(figsize=(7, 5))
ax = f.add_subplot(111)
tuple_out = probplot(boston.target - predictions_cv...