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results.txt
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[[570974 86]
[131 86684]]
precision recall f1-score support
0 0.99977 0.99985 0.99981 571060
1 0.99901 0.99849 0.99875 86815
accuracy 0.99967 657875
macro avg 0.99939 0.99917 0.99928 657875
weighted avg 0.99967 0.99967 0.99967 657875
['trained_models/friday-decision-tree-classifier.pkl']
-----
accuracy: 0.9995713471404142
[[570998 62]
[ 220 86595]]
precision recall f1-score support
0 0.99961 0.99989 0.99975 571060
1 0.99928 0.99747 0.99837 86815
accuracy 0.99957 657875
macro avg 0.99945 0.99868 0.99906 657875
weighted avg 0.99957 0.99957 0.99957 657875
Saved model to trained_models/knn-friday.pkl
-----------------
Linear SVM:
Accuracy score on Validation set:
0.9953603784659543
starting test
accuracy: 0.9953851415542466
[[568563 2497]
[ 539 86276]]
precision recall f1-score support
0 0.99905 0.99563 0.99734 571060
1 0.97187 0.99379 0.98271 86815
accuracy 0.99539 657875
macro avg 0.98546 0.99471 0.99002 657875
weighted avg 0.99547 0.99539 0.99541 657875
Saved model to trained_models/linsvm-friday.pkl
----------------
0.9993700486091252
---------------
Best performing hyperparameters on Validation set:
{'learning_rate': 1, 'n_estimators': 150}
---------------
AdaBoostClassifier(learning_rate=1, n_estimators=150, random_state=0)
starting test
accuracy: 0.9993357400722022
[[571019 41]
[ 396 86419]]
precision recall f1-score support
0 0.99931 0.99993 0.99962 571060
1 0.99953 0.99544 0.99748 86815
accuracy 0.99934 657875
macro avg 0.99942 0.99768 0.99855 657875
weighted avg 0.99934 0.99934 0.99934 657875
Saved model to trained_models/ada-friday.pkl
---------------
Logistic Regression
accuracy: 0.9853318461189084
[[1897796 4488]
[ 27678 262952]]
precision recall f1-score support
0 0.98563 0.99764 0.99160 1902284
1 0.98322 0.90477 0.94236 290630
accuracy 0.98533 2192914
macro avg 0.98442 0.95120 0.96698 2192914
weighted avg 0.98531 0.98533 0.98507 2192914