ENH Print metrics

luispedro · luispedro · commit 045d3a13ee8e · 2014-09-20T23:38:05.000+02:00
diff --git a/ch07/figure4.py b/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')

-Original file line number
+Diff line change
 # This script plots prediction-vs-actual on training set for the Boston dataset
 # using OLS regression
+-
 +importnumpyasnp
 fromsklearn.linear_modelimportLinearRegression
 fromsklearn.datasetsimportload_boston
 +fromsklearn.metricsimportmean_squared_error
 importpylabasplt
 boston=load_boston()
 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')