Chapter 3: Introduction to Machine Learning via Scikit-Learn

Activity 5: Generating Predictions and Evaluating the Performance of a Multiple Linear Regression Model

Solution:

1. Generate predictions on the test data using the following:

   predictions = model.predict(X_test)

   2.    Plot the predicted versus actual values on a scatterplot using the following code:

   import matplotlib.pyplot as plt

   from scipy.stats import pearsonr

    

   plt.scatter(y_test, predictions)

   plt.xlabel('Y Test (True Values)')

   plt.ylabel('Predicted Values')

   plt.title('Predicted vs. Actual Values (r = {0:0.2f})'.format(pearsonr(y_test, predictions)[0], 2))

   plt.show()

   Refer to the resultant output here:

   Figure 3.33: A scatterplot of predicted versus actual values from a multiple linear regression model

   Note

   There is a much stronger linear correlation between the predicted and actual values in the multiple linear regression model (r = 0.93) relative to the simple linear regression model...