diff --git a/examples/plot_random_dataset.py b/examples/plot_random_dataset.py
index bb48244c8cce6e27f478de8f74d8904b672eaf5c..7d54cd8f6e0bf0b7a7cdb5cb4120c99735a8007d 100644
--- a/examples/plot_random_dataset.py
+++ b/examples/plot_random_dataset.py
@@ -7,37 +7,40 @@ Plot several randomly generated 2D classification datasets.
 This example illustrates the `datasets.make_classification`
 function.
 
-Two binary and two multi-class classification datasets
-are generated, having either one informative and one random
-or two informative features.
+Three binary and two multi-class classification datasets
+are generated, with different numbers of informative
+features and clusters per class.
 """
+
 print __doc__
 
 import pylab as pl
 
 from sklearn.datasets import make_classification
 
-pl.figure(figsize=(14, 8))
-pl.subplot(221)
-pl.title("One informative feature, cluster")
+pl.figure(figsize=(8, 6))
+pl.subplots_adjust(bottom=.2, top=.95, left=.05, right=.95)
+
+ax1 = pl.subplot(221)
+pl.title("One informative feature, cluster", fontsize='small')
 X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=1,
         n_clusters_per_class=1)
 pl.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)
 
 pl.subplot(222)
-pl.title("Two informative features, one cluster")
+pl.title("Two informative features, one cluster", fontsize='small')
 X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=2,
         n_clusters_per_class=1)
 pl.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)
 
 pl.subplot(223)
-pl.title("Two informative features, two clusters")
+pl.title("Two informative features, two clusters", fontsize='small')
 X2, Y2 = make_classification(n_features=2, n_redundant=0, n_informative=2)
 pl.scatter(X2[:, 0], X2[:, 1], marker='o', c=Y2)
 
 
 pl.subplot(224)
-pl.title("Multi-class, two informative features,  one cluster")
+pl.title("Multi-class, two informative features,  one cluster", fontsize='small')
 X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=2,
         n_clusters_per_class=1, n_classes=3)
 pl.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)