Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set. You want to increase the AUC of the model. What should you do? Perform hyperparameter tuning B Train a classifier with deep neural networks, because neural networks would always beat SVMs C Deploy the model and measure the real-world AUC; it's always higher because of generalization D Scale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC
Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set. You want to increase the AUC of the model. What should you do?
Perform hyperparameter tuning
B Train a classifier with deep neural networks, because neural networks would always beat SVMs
C Deploy the model and measure the real-world AUC; it's always higher because of generalization
D Scale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC
Note : someone is saying A and someone is saying D. i need a correct answer with the proper justification
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