feature: add a comparison between all algorithms for each dataset to see which performs best

ML/aufgaben/comparison/compare_ml_algorihms.py

@@ -0,0 +1,59 @@
+"""
+Compare Decision Tree, Naive Bayes (supervised) and K-Means (unsupervised)
+on the Iris and Digits datasets using the same metrics.
+"""
+
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.naive_bayes import GaussianNB
+from sklearn.cluster import KMeans
+from sklearn.metrics import accuracy_score, classification_report, adjusted_rand_score
+import numpy as np
+
+
+def kmeans_accuracy(X, y, n_classes):
+    """Map each cluster to its majority true label, then compute accuracy."""
+    kmeans = KMeans(n_clusters=n_classes, init="k-means++", n_init=10, random_state=42)
+    kmeans.fit(X)
+    labels = np.zeros_like(kmeans.labels_)
+    for i in range(n_classes):
+        mask = kmeans.labels_ == i
+        if mask.sum() > 0:
+            labels[mask] = np.bincount(y[mask]).argmax()
+    return labels, kmeans
+
+
+def evaluate(name, dataset, target_names):
+    print(f"\n{'='*60}")
+    print(f" {name}")
+    print(f"{'='*60}")
+
+    X_train, X_test, y_train, y_test = train_test_split(
+        dataset.data, dataset.target, test_size=0.3, random_state=42
+    )
+
+    # supervised
+    for clf_name, clf in [("Decision Tree", DecisionTreeClassifier(random_state=42)),
+                          ("Naive Bayes", GaussianNB())]:
+        clf.fit(X_train, y_train)
+        y_pred = clf.predict(X_test)
+        print(f"\n--- {clf_name} ---")
+        print(f"Accuracy: {accuracy_score(y_test, y_pred):.3f}")
+        print(f"Adj. Rand: {adjusted_rand_score(y_test, y_pred):.3f}")
+        print(classification_report(y_test, y_pred, target_names=target_names))
+
+    # unsupervised (evaluated on full dataset)
+    n_classes = len(target_names)
+    mapped_labels, kmeans = kmeans_accuracy(dataset.data, dataset.target, n_classes)
+    print(f"\n--- K-Means (mapped) ---")
+    print(f"Accuracy: {accuracy_score(dataset.target, mapped_labels):.3f}")
+    print(f"Adj. Rand: {adjusted_rand_score(dataset.target, kmeans.labels_):.3f}")
+    print(classification_report(dataset.target, mapped_labels, target_names=target_names))
+
+
+iris = datasets.load_iris()
+evaluate("IRIS", iris, list(iris.target_names))
+
+digits = datasets.load_digits()
+evaluate("DIGITS", digits, [str(n) for n in digits.target_names])