Exam AWS Certified Machine Learning - Specialty topic 1 question 325 discussion

D. F1 score and E. True positive rate (Recall). F1 Score: This is the harmonic mean of precision and recall, providing a balance between the two. It is particularly useful when you need to balance the importance of both false positives and false negatives. True Positive Rate (Recall): This measures the proportion of actual fraudulent transactions that are correctly identified. High recall ensures that most fraudulent transactions are detected.

recall and f1

We need both precision and recall. The F1 score incorporates both of these metrics. and , the true positive rate which is only the recall.

E. True positive rate (also known as Recall or Sensitivity): True positive rate measures the proportion of actual fraudulent transactions that are correctly identified by the classifier. Maximizing the true positive rate ensures that as many fraudulent transactions as possible are captured by the model, reducing the number of false negatives. D. F1 score: F1 score is the harmonic mean of precision and recall. It provides a balance between precision (the ability of the classifier to correctly identify positive cases) and recall (the ability of the classifier to capture all positive cases). Maximizing the F1 score ensures a good balance between capturing fraudulent transactions (high recall) and minimizing false positives (high precision).

Review the Confusion matrix. Depending on your selected model score threshold, you can see the simulated impact based on a sample of 100,000 events. The distribution of fraud and legitimate events simulates the fraud rate in your businesses. Use this information to find the right balance between true positive rate and false positive rate. https://docs.aws.amazon.com/frauddetector/latest/ug/training-performance-metrics.html

F1 Score True Positive Rate, Recall, Sensitivity are all same thing

Metrics such as specificity (A), accuracy (C), and F1 score (D) are also important but may not directly prioritize the detection of fraudulent transactions. Specificity focuses on the proportion of non-fraudulent transactions correctly identified, accuracy measures overall correctness, and F1 score balances precision and recall. While these metrics are useful for evaluating the overall performance of the classifier, they may not be the primary focus when the goal is to detect as many fraudulent transactions as possible. Therefore, the most suitable metrics for optimizing the classifier to detect fraudulent transactions are False positive rate (B) and True positive rate (E).

These metrics will help the data scientist optimize the classifier to detect as many fraudulent transactions as possible.

- Maximizing TPR ensures that as many fraudulent transactions as possible are captured. - F1 score balances precision and recall and is useful when the class distribution is imbalanced (as in credit card fraud detection)