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 A. Binary classification confusion matrix. A binary classification confusion matrix displays the counts of True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN). Precision is calculated directly from these values as TP / (TP + FP), representing the proportion of positive predictions…

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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

ABinary classification confusion matrix
Bbox plot
CGradient descent
Dcoefficient of determination

How the community answered

(34 responses)

A
91% (31)
B
6% (2)
D
3% (1)

Explanation

A binary classification confusion matrix displays the counts of True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN). Precision is calculated directly from these values as TP / (TP + FP), representing the proportion of positive predictions that are actually correct. A box plot visualizes data distribution and spread. Gradient descent is an optimization algorithm, not an evaluation visualization. The coefficient of determination (R²) is a regression metric. Only the confusion matrix provides the raw counts needed to compute and interpret precision.

Topics

#Machine Learning Evaluation#Binary Classification#Precision#Confusion Matrix

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