diff --git a/examples/tmva/plot_multiclass.py b/examples/tmva/plot_multiclass.py
index ce42356..319c603 100644
--- a/examples/tmva/plot_multiclass.py
+++ b/examples/tmva/plot_multiclass.py
@@ -47,18 +47,18 @@
 # The following line is necessary if events have been added individually:
 factory.PrepareTrainingAndTestTree(TCut('1'), 'NormMode=EqualNumEvents')
 
-# Train a BDT
-factory.BookMethod('BDT', 'BDTG',
-                   'nCuts=20:NTrees=20:MaxDepth=4:'
-                   'BoostType=Grad:Shrinkage=0.10')
+# Train an MLP
+factory.BookMethod('MLP', 'MLP',
+                   'NeuronType=tanh:NCycles=200:HiddenLayers=N+2,2:'
+                   'TestRate=5:EstimatorType=MSE')
 factory.TrainAllMethods()
 
 # Classify the test dataset with the BDT
 reader = TMVA.Reader()
 for n in range(2):
     reader.AddVariable('f{0}'.format(n), array('f', [0.]))
-reader.BookMVA('BDT', 'weights/classifier_BDTG.weights.xml')
-class_proba = evaluate_reader(reader, 'BDT', X_test)
+reader.BookMVA('MLP', 'weights/classifier_MLP.weights.xml')
+class_proba = evaluate_reader(reader, 'MLP', X_test)
 
 # Plot the decision boundaries
 plot_colors = "rgb"
@@ -72,11 +72,10 @@
 xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
                      np.arange(y_min, y_max, plot_step))
 
-Z = evaluate_reader(reader, 'BDT', np.c_[xx.ravel(), yy.ravel()])
+Z = evaluate_reader(reader, 'MLP', np.c_[xx.ravel(), yy.ravel()])
 Z = np.argmax(Z, axis=1) - 1
 Z = Z.reshape(xx.shape)
-plt.contourf(xx, yy, Z, cmap=cmap, vmin=Z.min(), vmax=Z.max(),
-             levels=np.linspace(Z.min(), Z.max(), 50))
+plt.contourf(xx, yy, Z, cmap=cmap, alpha=0.5)
 plt.axis("tight")
 
 # Plot the training points