diff --git a/learning.ipynb b/learning.ipynb
index 86c84e475..16bb4bd6b 100644
--- a/learning.ipynb
+++ b/learning.ipynb
@@ -124,7 +124,7 @@
     "\n",
     "* **examples**: Holds the items of the dataset. Each item is a list of values.\n",
     "\n",
-    "* **attrs**: The indexes of the features (by default in the range of [0,f), where *f* is the number of features. For example, `item[i]` returns the feature at index *i* of *item*.\n",
+    "* **attrs**: The indexes of the features (by default in the range of [0,f), where *f* is the number of features). For example, `item[i]` returns the feature at index *i* of *item*.\n",
     "\n",
     "* **attrnames**: An optional list with attribute names. For example, `item[s]`, where *s* is a feature name, returns the feature of name *s* in *item*.\n",
     "\n",
@@ -1072,6 +1072,42 @@
     "</ol>"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example\n",
+    "\n",
+    "We will now use the Decision Tree Learner to classify a sample with values: 5.1, 3.0, 1.1, 0.1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "setosa\n"
+     ]
+    }
+   ],
+   "source": [
+    "iris = DataSet(name=\"iris\")\n",
+    "\n",
+    "DTL = DecisionTreeLearner(iris)\n",
+    "print(DTL([5.1, 3.0, 1.1, 0.1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As expected, the Decision Tree learner classifies the sample as \"setosa\" as seen in the previous section."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1760,7 +1796,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.6.3"
   }
  },
  "nbformat": 4,
diff --git a/learning.py b/learning.py
index f5bc5d835..0d3d3b110 100644
--- a/learning.py
+++ b/learning.py
@@ -542,7 +542,7 @@ def plurality_value(examples):
         return DecisionLeaf(popular)
 
     def count(attr, val, examples):
-        """Count the number of examples that have attr = val."""
+        """Count the number of examples that have example[attr] = val."""
         return sum(e[attr] == val for e in examples)
 
     def all_same_class(examples):