Google Professional Machine Learning Engineer
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As a professional working for a credit card company, you're tasked with developing a custom fraud detection model using AutoML Tables, based on historical data. Your primary goals are to maximize the detection of fraudulent transactions while keeping false positives to a minimum. The dataset is highly imbalanced, with fraudulent transactions representing less than 1% of all transactions. Additionally, the company emphasizes the importance of minimizing false positives to avoid customer dissatisfaction and unnecessary fraud investigations. Given these constraints, which optimization objective should you select for training your model to best meet the company's requirements? Choose one correct option.
A
An optimization objective that minimizes Log loss, focusing on overall prediction accuracy without specific emphasis on precision or recall.
B
An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value, balancing true positive rate and false positive rate equally.
C
An optimization objective that maximizes the Precision at a Recall value of 0.50, focusing on precision at a specific recall point.
D
An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value, emphasizing precision across all recall values, especially important for imbalanced datasets.