update

doc/src/week37/week37.do.txt

@@ -1102,11 +1102,6 @@ You may also find this recent "article":"https://www.pnas.org/content/116/32/158
 
 !split
 ===== Another Example from Scikit-Learn's Repository =====
-!bc pycod
-"""
-============================
-Underfitting vs. Overfitting
-============================
 
 This example demonstrates the problems of underfitting and overfitting and
 how we can use linear regression with polynomial features to approximate
@@ -1115,17 +1110,19 @@ which is a part of the cosine function. In addition, the samples from the
 real function and the approximations of different models are displayed. The
 models have polynomial features of different degrees. We can see that a
 linear function (polynomial with degree 1) is not sufficient to fit the
-training samples. This is called **underfitting**. A polynomial of degree 4
+training samples. This is called _underfitting_. A polynomial of degree 4
 approximates the true function almost perfectly. However, for higher degrees
-the model will **overfit** the training data, i.e. it learns the noise of the
+the model will _overfit_ the training data, i.e. it learns the noise of the
 training data.
-We evaluate quantitatively **overfitting** / **underfitting** by using
+We evaluate quantitatively overfitting and underfitting by using
 cross-validation. We calculate the mean squared error (MSE) on the validation
 set, the higher, the less likely the model generalizes correctly from the
 training data.
-"""
 
-print(__doc__)
+!bc pycod
+
+
+#print(__doc__)
 
 import numpy as np
 import matplotlib.pyplot as plt