You are evaluating a completed binary classification machine learning model. You need to use the precision as the evaluation metric. Which visualization should you use?

The correct answer is D. Receiver Operating Characteristic (ROC) curve. NOTE: The ROC curve does not directly plot precision. The ROC curve plots True Positive Rate (Recall/Sensitivity) on the Y-axis against False Positive Rate (1-Specificity) on the X-axis. Precision is best visualized using a Precision-Recall curve. However, among the choices…

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Question

You are evaluating a completed binary classification machine learning model. You need to use the precision as the evaluation metric. Which visualization should you use?

Options

Aviolin plot
BGradient descent
CScatter plot
DReceiver Operating Characteristic (ROC) curve

How the community answered

(28 responses)

B
7% (2)
C
4% (1)
D
89% (25)

Explanation

NOTE: The ROC curve does not directly plot precision. The ROC curve plots True Positive Rate (Recall/Sensitivity) on the Y-axis against False Positive Rate (1-Specificity) on the X-axis. Precision is best visualized using a Precision-Recall curve. However, among the choices given, the ROC curve (D) is the only valid binary classification evaluation visualization. A violin plot (A) shows data distributions, gradient descent (B) is an optimization algorithm (not a visualization for evaluation), and a scatter plot (C) does not measure classification performance. The exam likely intends the ROC curve as the answer because it is the standard classification evaluation tool listed, even though a Precision-Recall curve would be more precise for assessing precision specifically.

Topics

#Machine Learning Evaluation#Binary Classification#Precision#ROC Curve

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