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

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Aaron
2026-04-30 20:05:55 +02:00
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commit 28bb039e58
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ML/aufgaben/comparison/compare.txt
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============================================================
IRIS
============================================================
--- Decision Tree ---
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
--- Naive Bayes ---
Accuracy: 0.978
Adj. Rand: 0.943
precision recall f1-score support
setosa 1.00 1.00 1.00 19
versicolor 1.00 0.92 0.96 13
virginica 0.93 1.00 0.96 13
accuracy 0.98 45
macro avg 0.98 0.97 0.97 45
weighted avg 0.98 0.98 0.98 45
--- K-Means (mapped) ---
Accuracy: 0.893
Adj. Rand: 0.730
precision recall f1-score support
setosa 1.00 1.00 1.00 50
versicolor 0.77 0.96 0.86 50
virginica 0.95 0.72 0.82 50
accuracy 0.89 150
macro avg 0.91 0.89 0.89 150
weighted avg 0.91 0.89 0.89 150
============================================================
DIGITS
============================================================
--- Decision Tree ---
Accuracy: 0.843
Adj. Rand: 0.685
precision recall f1-score support
0 0.92 0.91 0.91 53
1 0.74 0.78 0.76 50
2 0.83 0.74 0.79 47
3 0.78 0.85 0.81 54
4 0.81 0.85 0.83 60
5 0.92 0.86 0.89 66
6 0.93 0.94 0.93 53
7 0.85 0.84 0.84 55
8 0.89 0.77 0.82 43
9 0.78 0.85 0.81 59
accuracy 0.84 540
macro avg 0.85 0.84 0.84 540
weighted avg 0.85 0.84 0.84 540
--- Naive Bayes ---
Accuracy: 0.852
Adj. Rand: 0.710
precision recall f1-score support
0 1.00 0.98 0.99 53
1 0.86 0.74 0.80 50
2 0.86 0.66 0.75 47
3 0.95 0.76 0.85 54
4 0.98 0.85 0.91 60
5 0.94 0.94 0.94 66
6 0.89 0.96 0.93 53
7 0.72 0.98 0.83 55
8 0.57 0.91 0.70 43
9 0.89 0.71 0.79 59
accuracy 0.85 540
macro avg 0.87 0.85 0.85 540
weighted avg 0.88 0.85 0.85 540
--- K-Means (mapped) ---
Accuracy: 0.794
Adj. Rand: 0.667
precision recall f1-score support
0 0.99 0.99 0.99 178
1 0.62 0.30 0.41 182
2 0.84 0.84 0.84 177
3 0.86 0.85 0.85 183
4 0.99 0.92 0.95 181
5 0.87 0.75 0.81 182
6 0.97 0.98 0.98 181
7 0.86 0.95 0.90 179
8 0.45 0.59 0.51 174
9 0.58 0.77 0.66 180
accuracy 0.79 1797
macro avg 0.80 0.79 0.79 1797
weighted avg 0.80 0.79 0.79 1797
ML/aufgaben/comparison/compare_ml_algorihms.py
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"""
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])