Update app.py

app.py

@@ -99,13 +99,13 @@ def iter_grid(n_rows, n_cols):
                     yield
 
 info = '''
-## Plot the decision surfaces of ensembles of trees on the Iris dataset
+# Plot the decision surfaces of ensembles of trees on the Iris dataset
 
-This plot compares the decision surfaces learned by a decision tree classifier, a random forest classifier, an extra-trees classifier, and by an AdaBoost classifier.
+This plot compares the **decision surfaces** learned by a decision tree classifier, a random forest classifier, an extra-trees classifier, and by an AdaBoost classifier.
 
-There are in total four features in the Iris dataset. In this example you can select two features at a time for visualization purposes using the dropdown box below.
+There are in total **four features** in the Iris dataset. In this example you can select **two features at a time** for visualization purposes using the dropdown box below. All features are normalized to zero mean and unit standard deviation.
 
-You can also vary the number of estimators in the ensembles and the max depth of the trees using the sliders.
+Play around with the **number of estimators** in the ensembles and the **max depth** of the trees using the sliders.
 '''
 
 with gr.Blocks() as demo: