Question: In a confusion matrix, what does the term \'specificity\' refer to?
Options:
True Positive Rate
False Positive Rate
True Negative Rate
False Negative Rate
Correct Answer: True Negative Rate
Solution:
Specificity is the True Negative Rate, indicating the proportion of actual negatives that are correctly identified.
In a confusion matrix, what does the term 'specificity' refer to?
Practice Questions
Q1
In a confusion matrix, what does the term 'specificity' refer to?
True Positive Rate
False Positive Rate
True Negative Rate
False Negative Rate
Questions & Step-by-Step Solutions
In a confusion matrix, what does the term 'specificity' refer to?
Step 1: Understand what a confusion matrix is. It is a table used to evaluate the performance of a classification model by comparing predicted and actual values.
Step 2: Identify the four components of a confusion matrix: True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN).
Step 3: Focus on True Negatives (TN). These are the cases where the model correctly predicts the negative class.
Step 4: Understand that specificity measures how well the model identifies negative cases. It tells us the proportion of actual negatives that are correctly identified as negatives.
Step 5: Use the formula for specificity: Specificity = TN / (TN + FP). This means you divide the number of true negatives by the total number of actual negatives (true negatives plus false positives).
Step 6: Conclude that a higher specificity means the model is better at correctly identifying negative cases.
Specificity – Specificity is the measure of a test's ability to correctly identify true negatives among all actual negatives.
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