@@ -16,8 +16,7 @@ boston1.py
 boston1numpy.py
     Version of above script using numpy operations for linear regression
 boston_cv_penalized.py
-    Test different penalized regression schemes on the Boston dataset
-cv10_lr.py
+    Test different penalized (and OLS) regression schemes on the Boston dataset
 figure1_2.py
     Show the regression line for Boston data
 figure3.py
 
@@ -10,17 +10,18 @@
 from __future__ import print_function
 import numpy as np
 from sklearn.cross_validation import KFold
-from sklearn.linear_model import ElasticNet, Lasso, Ridge
+from sklearn.linear_model import LinearRegression, ElasticNet, Lasso, Ridge
 from sklearn.metrics import r2_score
 from sklearn.datasets import load_boston
 boston = load_boston()
 x = boston.data
 y = boston.target
 
 for name, met in [
-        ('elastic-net(.5)', ElasticNet(fit_intercept=True, alpha=0.5)),
-        ('lasso(.5)', Lasso(fit_intercept=True, alpha=0.5)),
-        ('ridge(.5)', Ridge(fit_intercept=True, alpha=0.5)),
+        ('linear regression', LinearRegression()),
+        ('elastic-net(.5)', ElasticNet(alpha=0.5)),
+        ('lasso(.5)', Lasso(alpha=0.5)),
+        ('ridge(.5)', Ridge(alpha=0.5)),
 ]:
     # Fit on the whole data:
     met.fit(x, y)