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Your team is currently engaged in a machine learning project aimed at solving a binary classification problem. Recently, you have trained a Support Vector Machine (SVM) classifier using its default parameter settings. After evaluating the model's performance on the validation set, you achieved an Area Under the Curve (AUC) score of 0.87. To improve the predictive performance of the model, particularly to increase the AUC, what steps should you consider taking?
A
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