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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