feature(algo): add randomforest algorithm to the comparison

ML/aufgaben/comparison/compare.txt

@@ -31,6 +31,20 @@ Adj. Rand: 0.943
 weighted avg       0.98      0.98      0.98        45
 
 
+--- Random Forest ---
+Accuracy: 1.000
+Adj. Rand: 1.000
+              precision    recall  f1-score   support
+
+      setosa       1.00      1.00      1.00        19
+  versicolor       1.00      1.00      1.00        13
+   virginica       1.00      1.00      1.00        13
+
+    accuracy                           1.00        45
+   macro avg       1.00      1.00      1.00        45
+weighted avg       1.00      1.00      1.00        45
+
+
 --- K-Means (mapped) ---
 Accuracy: 0.893
 Adj. Rand: 0.730
@@ -91,6 +105,27 @@ Adj. Rand: 0.710
 weighted avg       0.88      0.85      0.85       540
 
 
+--- Random Forest ---
+Accuracy: 0.976
+Adj. Rand: 0.946
+              precision    recall  f1-score   support
+
+           0       1.00      0.98      0.99        53
+           1       0.96      1.00      0.98        50
+           2       1.00      1.00      1.00        47
+           3       0.98      0.96      0.97        54
+           4       0.97      1.00      0.98        60
+           5       0.97      0.95      0.96        66
+           6       0.98      0.98      0.98        53
+           7       0.98      0.98      0.98        55
+           8       0.95      0.95      0.95        43
+           9       0.97      0.95      0.96        59
+
+    accuracy                           0.98       540
+   macro avg       0.98      0.98      0.98       540
+weighted avg       0.98      0.98      0.98       540
+
+
 --- K-Means (mapped) ---
 Accuracy: 0.794
 Adj. Rand: 0.667

ML/aufgaben/comparison/compare_ml_algorihms.py

@@ -15,6 +15,7 @@ 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.ensemble import RandomForestClassifier
 from sklearn.metrics import accuracy_score, classification_report, adjusted_rand_score
 
 import numpy as np
@@ -64,6 +65,7 @@ def evaluate(name, dataset, target_names):
     for classifier_name, classifier in [
         ("Decision Tree", DecisionTreeClassifier(random_state=42)),
         ("Naive Bayes", GaussianNB()),
+        ("Random Forest", RandomForestClassifier(n_estimators=100, random_state=42)),
     ]:
         classifier.fit(X_train, y_train)
         y_pred = classifier.predict(X_test)