In a confusion matrix, what does the term 'specificity' refer to?
Practice Questions
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Q1
In a confusion matrix, what does the term 'specificity' refer to?
True Positive Rate
False Positive Rate
True Negative Rate
False Negative Rate
Specificity is the True Negative Rate, indicating the proportion of actual negatives that are correctly identified.
Questions & Step-by-step Solutions
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Q
Q: In a confusion matrix, what does the term 'specificity' refer to?
Solution: Specificity is the True Negative Rate, indicating the proportion of actual negatives that are correctly identified.
Steps: 6
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.
