DSA-C03 Exam Dumps | You have a binary classification model deployed in Snowflake to predict customer churn. The model outputs

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Question 69/143

You have a binary classification model deployed in Snowflake to predict customer churn. The model outputs a probability score between 0 and 1. You've calculated the following confusion matrix on a holdout set: I I Predicted Positive I Predicted Negative I --1 1 Actual Positive | 80 | 20 | I Actual Negative | 10 | 90 | What are the Precision, Recall, and Accuracy for this model, and what do these metrics tell you about the model's performance? SELECT statement given for true and false condition (True Positive, True Negative, False Positive, False Negative)

A. Precision = 0.89, Recall = 0.80, Accuracy = 0.85. The model has good overall performance with balanced precision and recall.

B. Precision = 0.80, Recall = 0.89, Accuracy = 0.85. The model is slightly better at identifying true positives than avoiding false positives.

C. Precision = 0.89, Recall = 0.80, Accuracy = 0.85. The model is slightly better at avoiding false positives than identifying true positives.

D. Precision = 0.90, Recall = 0.80, Accuracy = 0.80. The model has good overall performance but needs to be adjusted to improve the false negative rate.

E. Precision = 0.80, Recall = 0.90, Accuracy = 0.90. The model is performing poorly, with a high rate of both false positives and false negatives.

Correct Answer: C

The correct answer is C. Precision is calculated as True Positives / (True Positives + False Positives) = 80 / (80 + 10) = 0.89. Recall is calculated as True Positives / (True Positives + False Negatives) = 80 / (80 + 20) = 0.80. Accuracy is calculated as (True Positives + True Negatives) / Total = (80 + 90) / 200 = 0.85. High precision indicates fewer false positives, while lower recall indicates more false negatives. Also the select statement calculates true positives, true negatives, false positives, and false negatives from churn_predictions table and then accuracy, precision , recall has to be calculated.

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