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Answer: Perform hyperparameter tuning
The correct answer is A. Performing hyperparameter tuning on your support vector machine (SVM) classifier can help you find the optimal parameters that might improve the model's performance, thereby increasing the AUC. Other options, such as using deep neural networks (B) or deploying the model to measure real-world AUC (C), are either overly complex or misleading. Option D, which suggests scaling predictions, does not reliably lead to genuine improvements in AUC. Hyperparameter tuning is a recommended approach for enhancing the performance of your model.
Author: LeetQuiz Editorial Team
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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
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