You work for a credit card company and have been asked to create a custom fraud detection model based on historical data using AutoML Tables.
You need to prioritize detection of fraudulent transactions while minimizing false positives.
Which optimization objective should you use when training the model?
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A. B. C. D.C.
When designing a fraud detection model, the goal is to identify as many fraudulent transactions as possible while minimizing the number of false positives (transactions incorrectly identified as fraudulent). AutoML Tables is a tool that automates the process of creating a machine learning model that best suits your specific data and task. In order to do this, it uses an optimization objective that the user selects to guide the model's training and evaluation.
The four optimization objectives listed in the question are all commonly used in machine learning models. However, each objective has different strengths and weaknesses depending on the specific task at hand.
Option A: Minimizing Log loss is a common objective in classification tasks where the goal is to minimize the difference between predicted and actual probabilities of a binary outcome. This objective is a good choice when the costs of false positives and false negatives are similar. However, in a fraud detection task, false positives can result in real customers having their transactions blocked or flagged, leading to inconvenience and customer dissatisfaction.
Option B: Maximizing Precision at a Recall value of 0.50 is an objective that focuses on maximizing the number of true positives while maintaining a specific level of recall (the percentage of actual fraud cases that are correctly identified by the model). While this objective can be useful, it only maximizes precision at one specific recall value, which may not be optimal for all fraud detection scenarios.
Option C: Maximizing the area under the precision-recall curve (AUC PR) is an objective that balances precision and recall across all possible thresholds of the model's output. This objective is a good choice for imbalanced datasets, where there are many more negative examples than positive examples (e.g. very few fraud cases compared to the total number of transactions). By maximizing the AUC PR, the model can find a balance between correctly identifying fraud cases and minimizing false positives.
Option D: Maximizing the area under the receiver operating characteristic curve (AUC ROC) is an objective that balances the true positive rate and the false positive rate across all possible thresholds of the model's output. This objective is a common choice in binary classification tasks and is especially useful when the cost of false positives and false negatives are not equal. In a fraud detection scenario, minimizing false positives is crucial, making the AUC ROC a suboptimal objective.
Therefore, the best option for this specific task of prioritizing detection of fraudulent transactions while minimizing false positives is option C, an optimization objective that maximizes the area under the precision-recall curve (AUC PR) value. By maximizing AUC PR, the model will be able to balance precision and recall across all possible thresholds and find a balance between correctly identifying fraud cases and minimizing false positives.