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results.txt
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Results:
Random Forest:
precision recall f1-score support
1 0.85 0.85 0.85 175
2 0.75 0.87 0.81 95
3 0.90 0.84 0.87 223
accuracy 0.85 493
macro avg 0.83 0.85 0.84 493
weighted avg 0.85 0.85 0.85 493
GridSearchCV:
RandomForestClassifier(max_depth=9, max_leaf_nodes=9, n_estimators=50)
RandomForestClassifier(max_depth=6, max_leaf_nodes=9, n_estimators=150) // bi daha çalıştırınca böyle geldi test performansı düşüyo %73 e (cv=5)
Seçili Classifierlarla: (pek değişmemiş)
precision recall f1-score support
1 0.85 0.86 0.85 173
2 0.78 0.86 0.82 100
3 0.91 0.86 0.88 220
accuracy 0.86 493
macro avg 0.85 0.86 0.85 493
weighted avg 0.86 0.86 0.86 493
RandomizedSearchCV:
RandomForestClassifier(max_depth=6, max_leaf_nodes=9, n_estimators=25)
RandomForestClassifier(max_depth=9, max_features='log2', max_leaf_nodes=9, n_estimators=50) (cv=5 iken)
1.
precision recall f1-score support
1 0.86 0.86 0.86 175
2 0.79 0.88 0.83 99
3 0.92 0.88 0.90 219
accuracy 0.87 493
macro avg 0.86 0.87 0.86 493
weighted avg 0.88 0.87 0.87 493
2. (cv=5 iken)
precision recall f1-score support
1 0.84 0.85 0.85 172
2 0.78 0.85 0.82 101
3 0.91 0.86 0.89 220
accuracy 0.86 493
macro avg 0.85 0.86 0.85 493
weighted avg 0.86 0.86 0.86 493
SVM Linear Soft Margin With 2 Classes: (classes 1 and 2)
Classification Report:
precision recall f1-score support
-1 0.81 0.86 0.83 152
1 0.83 0.78 0.81 141
accuracy 0.82 293
macro avg 0.82 0.82 0.82 293
weighted avg 0.82 0.82 0.82 293
Accuracy: 81.91%
SVM Linear Soft Margin With 2 Classes with PCA Feature Selection: (classes 2 and 3)
Classification Report for 2D Test Set:
precision recall f1-score support
-1 0.84 0.89 0.87 133
1 0.93 0.89 0.91 204
accuracy 0.89 337
macro avg 0.89 0.89 0.89 337
weighted avg 0.90 0.89 0.89 337
Accuracy on 2D Test Set: 89.32%
Random Forest Last Results:
Training + testing runtime: 0.48s
precision recall f1-score support
1 0.85 0.87 0.86 172
2 0.77 0.84 0.81 101
3 0.91 0.86 0.88 220
accuracy 0.86 493
macro avg 0.84 0.86 0.85 493
weighted avg 0.86 0.86 0.86 493
RandomForestClassifier(n_estimators=200)
Time taken for grid search: 459.46s
precision recall f1-score support
1 0.84 0.84 0.84 175
2 0.77 0.87 0.82 98
3 0.91 0.86 0.88 220
accuracy 0.85 493
macro avg 0.84 0.86 0.85 493
weighted avg 0.86 0.85 0.85 493
Training + testing runtime: 1.36 seconds
RandomForestClassifier(max_depth=9, max_features='log2', n_estimators=200)
Time taken for random search: 16.01s
precision recall f1-score support
1 0.83 0.82 0.83 178
2 0.76 0.88 0.82 95
3 0.89 0.85 0.87 220
accuracy 0.84 493
macro avg 0.83 0.85 0.84 493
weighted avg 0.85 0.84 0.84 493
Training + testing runtime: 1.24 seconds
Linear Soft SVM C=1
Training time: 5.43s
Classification Report:
precision recall f1-score support
-1 0.81 0.86 0.83 152
1 0.83 0.78 0.81 141
accuracy 0.82 293
macro avg 0.82 0.82 0.82 293
weighted avg 0.82 0.82 0.82 293
Accuracy: 81.91%
Training time: 41.65s for C=0.1
Classification Report:
precision recall f1-score support
1 0.65 0.43 0.52 175
2 0.65 0.78 0.71 110
3 0.67 0.79 0.73 208
accuracy 0.66 493
macro avg 0.66 0.67 0.65 493
weighted avg 0.66 0.66 0.65 493
Accuracy: 66.13%
Training time: 52.15s for C=1
Classification Report:
precision recall f1-score support
1 0.68 0.43 0.53 175
2 0.66 0.77 0.71 110
3 0.68 0.83 0.75 208
accuracy 0.68 493
macro avg 0.67 0.68 0.66 493
weighted avg 0.68 0.68 0.66 493
Accuracy: 67.55%
Training time: 49.55s for C=10
Classification Report:
precision recall f1-score support
1 0.68 0.43 0.53 175
2 0.66 0.77 0.71 110
3 0.68 0.83 0.75 208
accuracy 0.68 493
macro avg 0.67 0.68 0.66 493
weighted avg 0.68 0.68 0.66 493
Accuracy: 67.55%
Training time: 64.11s for C=20
Classification Report:
precision recall f1-score support
1 0.64 0.43 0.52 175
2 0.67 0.75 0.70 110
3 0.68 0.83 0.75 208
accuracy 0.67 493
macro avg 0.66 0.67 0.66 493
weighted avg 0.67 0.67 0.66 493
Accuracy: 66.94%
SVM scikit grid (random kötü)
Training SVM with linear kernel...
Best parameters: {'C': 0.1, 'kernel': 'linear'}
Classification Report:
precision recall f1-score support
1 0.61 0.61 0.61 175
2 0.75 0.52 0.61 110
3 0.63 0.74 0.68 208
accuracy 0.64 493
macro avg 0.66 0.62 0.63 493
weighted avg 0.65 0.64 0.64 493
Accuracy: 0.640973630831643
Training SVM with rbf kernel...
Best parameters: {'C': 10, 'gamma': 'scale', 'kernel': 'rbf'}
Classification Report:
precision recall f1-score support
1 0.75 0.82 0.78 175
2 0.74 0.72 0.73 110
3 0.82 0.77 0.79 208
accuracy 0.77 493
macro avg 0.77 0.77 0.77 493
weighted avg 0.78 0.77 0.77 493
Accuracy: 0.7748478701825557
Training SVM with poly kernel...
Best parameters: {'C': 20, 'degree': 3, 'gamma': 'auto', 'kernel': 'poly'}
Classification Report:
precision recall f1-score support
1 0.72 0.81 0.76 175
2 0.69 0.61 0.65 110
3 0.78 0.75 0.76 208
accuracy 0.74 493
macro avg 0.73 0.72 0.72 493
weighted avg 0.74 0.74 0.73 493
Accuracy: 0.7363083164300203
Training SVM with sigmoid kernel...
Best parameters: {'C': 0.1, 'gamma': 'scale', 'kernel': 'sigmoid'}
Classification Report:
precision recall f1-score support
1 0.56 0.63 0.59 175
2 0.77 0.49 0.60 110
3 0.62 0.67 0.65 208
accuracy 0.62 493
macro avg 0.65 0.60 0.61 493
weighted avg 0.63 0.62 0.62 493
Accuracy: 0.6186612576064908