chroe(a1): update excercise, use python instead of jupyter

ML/aufgaben/a1/decisiontree_iris.ipynb

@@ -1259,6 +1259,13 @@
     "                         filled=True, rounded=True,  \n",
     "                         special_characters=True)         "
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -1277,7 +1284,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.12"
+   "version": "3.14.4"
   }
  },
  "nbformat": 4,

ML/aufgaben/a1/decisiontree_iris.py

@@ -0,0 +1,38 @@
+"""
+Use a decision tree classifier to predict flowers based on sepal and petal length/width
+"""
+
+import matplotlib.pyplot as plt
+
+from sklearn import datasets
+from sklearn.tree import DecisionTreeClassifier
+from sklearn import tree
+
+# load the iris data set and look at its dimensions
+iris = datasets.load_iris()
+print(iris.data.size)
+print(iris.target.size)
+print(iris.feature_names)
+print(iris.target_names)
+
+# use a decition tree classifier
+classifier = DecisionTreeClassifier()
+# use all but the last sample for training
+classifier.fit(iris.data[:-1], iris.target[:-1])
+
+# use the model to predict the last data sample
+last_sample = iris.data[-1:]
+last_target = iris.target[-1:]
+print(f"predicted: {last_sample} vs real: {last_target}")
+
+# print the tree for visual inspection
+fig, ax = plt.subplots(figsize=(20, 10))
+tree.plot_tree(
+    classifier,
+    feature_names=iris.feature_names,
+    class_names=iris.target_names,
+    filled=True,
+    rounded=True,
+    ax=ax,
+)
+fig.savefig("tree.png", dpi=150, bbox_inches="tight")