DSA-C03 Exam Dumps | You are building a fraud detection model in Snowflake using Snowpark Python. You want to evaluate the

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Question 24/143

You are building a fraud detection model in Snowflake using Snowpark Python. You want to evaluate the model's performance, particularly focusing on identifying instances of fraud (minority class). Which combination of metrics provides the most comprehensive assessment for this imbalanced classification problem within the Snowflake environment, considering the need to minimize both false positives (legitimate transactions flagged as fraudulent) and false negatives (fraudulent transactions missed)?

A. Accuracy and Recall.

B. Precision and Fl-score.

C. ROC AUC and Recall.

D. Precision, Recall, and Fl-score.

E. Accuracy and ROC AUC.

Correct Answer: D

Option D (Precision, Recall, and Fl-score) is the most comprehensive. In an imbalanced classification problem like fraud detection, accuracy alone is misleading because a model can achieve high accuracy by simply predicting the majority class (non-fraud) for all instances. Precision measures the proportion of correctly identified fraudulent transactions out of all transactions flagged as fraudulent. Recall measures the proportion of correctly identified fraudulent transactions out of all actual fraudulent transactions. F 1-score is the harmonic mean of precision and recall, providing a balanced measure of the model's performance. All three metrics are needed to comprehensively evaluate a fraud detection model on an imbalanced dataset.

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