cleaning up

doc/src/week44/week44.do.txt

@@ -742,12 +742,6 @@ display(grades)
 X = grades.loc[:, grades.columns != 'Grade'].values
 y = grades.loc[:, grades.columns == 'Grade'].values
 print(X)
-# Create the encoder.
-encoder = OneHotEncoder(handle_unknown="ignore")
-# Assume for simplicity all features are categorical.
-encoder.fit(X)    
-# Apply the encoder.
-X = encoder.transform(X)
 # Then do a Classification tree
 tree_clf = DecisionTreeClassifier(max_depth=2)
 tree_clf.fit(X, y)
@@ -763,9 +757,6 @@ cmd = 'dot -Tpng DataFiles/grade.dot -o DataFiles/grades.png'
 os.system(cmd)
 
 
-#data_pandas = pd.DataFrame(data,index=['Frodo','Bilbo','Aragorn','Sam'])
-#df.columns = ['First', 'Second', 'Third', 'Fourth', 'Fifth']
-
 !ec
 
 
@@ -1525,8 +1516,8 @@ for degree in range(1,maxdepth):
     print('Var:', variance[degree])
     print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
  
-mse_simpletree= np.mean( np.mean((y_test - simpleprediction)**2)
-print(mse_simpletree)
+mse_simpletree= np.mean( np.mean((y_test - simpleprediction)**2))
+print("Simple tree:",mse_simpletree)
 plt.xlim(1,maxdepth)
 plt.plot(polydegree, error, label='MSE')
 plt.plot(polydegree, bias, label='bias')