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tried_models.txt
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tried_models.txt
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1. Logistic Regression:
Sample True Label:
['STAR', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Sample Predicted Label:
['STAR', 'STAR', 'GALAXY', 'QSO', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Accuracy of the model is 0.9571459235596409
The Model is not Overfitted
Precision = 0.9561595530364684, Recall = 0.956148436217629, F1 Score = 0.9557782522908862
Conclusion: Acceptable
2. Decision Tree Classifier:
Sample True Label:
['STAR', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Sample Predicted Label:
['STAR', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Accuracy of the model is 1.0
The Model is Overfitted
Precision = 0.9642909285402042, Recall = 0.964317731284486, F1 Score = 0.9643040068955235
Conclusion: Rejected due to overfitting. See although the model is overfitted, precision, recall and F1 score are not exactly 1. This is because, we have used 3-fold cross validation.
3. Random Forest Classifier:
Sample True Label:
['STAR', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Sample Predicted Label:
['STAR', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Accuracy of the model is 0.9999747471558984
The Model is not Overfitted
Precision = 0.9763232429389745, Recall = 0.9764517228752888, F1 Score = 0.9762976502815489
Conclusion: Rejected
4. Support Vector Classifier:
Sample True Label:
['STAR', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Sample Predicted Label:
['STAR', 'STAR', 'GALAXY', 'QSO', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Accuracy of the model is 0.963673783759896
The Model is not Overfitted
Precision = 0.9618960797015264, Recall = 0.9614262806348565, F1 Score = 0.961243135495957
Conclusion: Rejected due to extremely slow computation
5. K Neighbors Classifier:
Sample True Label:
['STAR', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Sample Predicted Label:
['STAR', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Accuracy of the model is 0.9555802472253437
The Model is not Overfitted
Precision = 0.9352428392362788, Recall = 0.9349613000164143, F1 Score = 0.9349301040278498
Conclusion: Acceptable
6. Guassian Naive Bayes:
Sample True Label:
['STAR', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Sample Predicted Label:
[np.str_('STAR'), np.str_('STAR'), np.str_('GALAXY'), np.str_('QSO'), np.str_('STAR'), np.str_('GALAXY'), np.str_('GALAXY'), np.str_('STAR'), np.str_('GALAXY'), np.str_('QSO')]
Accuracy of the model is 0.9235596409045569
The Model is not Overfitted
Precision = 0.9277257316060743, Recall = 0.923496508794303, F1 Score = 0.9247480595718996
Conclusion: Acceptable
7. Multilayer Perceptron(MLP) Classifier:
Sample True Label:
['STAR', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Sample Predicted Label:
[np.str_('STAR'), np.str_('STAR'), np.str_('GALAXY'), np.str_('QSO'), np.str_('STAR'), np.str_('GALAXY'), np.str_('GALAXY'), np.str_('STAR'), np.str_('GALAXY'), np.str_('GALAXY')]
Accuracy of the model is 0.9739643177312844
The Model is not Overfitted
Precision = 0.9717043182763538, Recall = 0.9717546938723973, F1 Score = 0.9716142990019011
Conclusion: Acceptable
8. Quadratic Discriminant Analysis:
Sample True Label:
['STAR', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Sample Predicted Label:
['STAR', 'STAR', 'GALAXY', 'QSO', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Accuracy of the model is 0.9487872321620222
The Model is not Overfitted
Precision = 0.9501957545888445, Recall = 0.948623088675362, F1 Score = 0.9490844717210375
Conclusion: Acceptable
9. Bagging Classifier:
Sample True Label:
['STAR', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Sample Predicted Label:
['STAR', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Accuracy of the model is 0.9970580436621674
The Model is not Overfitted
Precision = 0.97597145363877, Recall = 0.9760981830578669, F1 Score = 0.9759269710725322
Conclusion: Acceptable
10. Gradient Boosting Classifier:
Sample True Label:
['STAR', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Sample Predicted Label:
['STAR', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Accuracy of the model is 0.976615866361949
The Model is not Overfitted
Precision = 0.9744785200798246, Recall = 0.9746082652558744, F1 Score = 0.9743894826110302
Conclusion: Rejected due to extremely slow computation
11. Extra Trees Classifier:
Sample True Label:
['STAR', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Sample Predicted Label:
['STAR', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Accuracy of the model is 1.0
The Model is Overfitted
Precision = 0.9715046327468476, Recall = 0.9715779239636864, F1 Score = 0.9713881194475238
Conclusion: Rejected due to overfitting. See although the model is overfitted, precision, recall and F1 score are not exactly 1. This is because, we have used 3-fold cross validation.
12. Ada Boost Classifier:
Sample True Label:
['STAR', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY', 'STAR', 'GALAXY', 'GALAXY']
Sample Predicted Label:
['GALAXY', 'GALAXY', 'GALAXY', 'GALAXY', 'GALAXY', 'GALAXY', 'GALAXY', 'GALAXY', 'GALAXY', 'GALAXY']
Accuracy of the model is 0.6434298412858748
The Model is not Overfitted
Precision = 0.6961963301353492, Recall = 0.7755148423591207, F1 Score = 0.7094704941068702
Conclusion: Rejected due to poor F1 score.