diff --git a/examples/tree/plot_iris.py b/examples/tree/plot_iris.py
index d1b6e25b59a1c..f299aab18d7d1 100644
--- a/examples/tree/plot_iris.py
+++ b/examples/tree/plot_iris.py
@@ -22,7 +22,7 @@
 
 # Parameters
 n_classes = 3
-plot_colors = "bry"
+plot_colors = "ryb"
 plot_step = 0.02
 
 # Load data
@@ -44,23 +44,22 @@
     y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
     xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
                          np.arange(y_min, y_max, plot_step))
+    plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)
 
     Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
     Z = Z.reshape(xx.shape)
-    cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
+    cs = plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu)
 
     plt.xlabel(iris.feature_names[pair[0]])
     plt.ylabel(iris.feature_names[pair[1]])
-    plt.axis("tight")
 
     # Plot the training points
     for i, color in zip(range(n_classes), plot_colors):
         idx = np.where(y == i)
         plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],
-                    cmap=plt.cm.Paired)
-
-    plt.axis("tight")
+                    cmap=plt.cm.RdYlBu, edgecolor='black', s=15)
 
 plt.suptitle("Decision surface of a decision tree using paired features")
-plt.legend()
+plt.legend(loc='lower right', borderpad=0, handletextpad=0)
+plt.axis("tight")
 plt.show()