Google Cloud Professional Data Engineer — Question 99

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?

Answer options

• 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

Correct answer: A

Explanation

The correct answer is A because hyperparameter tuning can help improve the performance of the SVM by optimizing its parameters, which can lead to a higher AUC. Option B is incorrect as deep neural networks do not always outperform SVMs, depending on the dataset. Option C is misleading since real-world AUC does not guarantee improvement and can vary. Option D is not a standard approach to improve AUC and does not address the model's underlying performance directly.