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 @@
""
]
},
+ {
+ "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):
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