ENH Print metrics

Author: luispedro

ch07/figure4.py

@@ -8,9 +8,10 @@
 
 # This script plots prediction-vs-actual on training set for the Boston dataset
 # using OLS regression
-
+import numpy as np
 from sklearn.linear_model import LinearRegression
 from sklearn.datasets import load_boston
+from sklearn.metrics import mean_squared_error
 import pylab as plt
 
 boston = load_boston()
@@ -21,6 +22,8 @@
 lr = LinearRegression()
 lr.fit(x, y)
 p = lr.predict(x)
+print("RMSE: {:.2}.".format(np.sqrt(mean_squared_error(y, p))))
+print("R2: {:.2}.".format(lr.score(x, y)))
 plt.scatter(p, y)
 plt.xlabel('Predicted price')
 plt.ylabel('Actual price')