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What does the precision evaluation metric primarily take into account?
- a)True Positives and False Negatives
- b)True Positives and False Positives
- c)True Negatives and False Positives
- d)True Negatives and False Negatives
Correct answer is option 'B'. Can you explain this answer?
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Most Upvoted Answer
What does the precision evaluation metric primarily take into account?...
Precision primarily takes into account True Positives and False Positives. It measures the accuracy of positive predictions made by the model.
Community Answer
What does the precision evaluation metric primarily take into account?...
Understanding Precision
Precision is a crucial evaluation metric in classification problems, particularly in contexts like medical diagnoses or spam detection, where false positives can lead to significant issues.
What is Precision?
Precision quantifies the accuracy of the positive predictions made by a model. It reflects how many of the predicted positive cases were actually positive.
Formula for Precision
Precision is calculated using the formula:
Precision = True Positives / (True Positives + False Positives)
Key Components of Precision
- True Positives (TP): These are the cases where the model correctly predicts the positive class.
- False Positives (FP): These are the instances where the model incorrectly predicts the positive class when it is actually negative.
Why Option B is Correct?
The correct answer is option 'B' because:
- Precision specifically focuses on True Positives and False Positives.
- It answers the question: "Of all instances predicted as positive, how many are actually positive?"
Implications of False Positives
- High precision indicates a low rate of false positives, which is essential in scenarios where false alarms can lead to unnecessary actions or anxiety.
- For example, in spam detection, if a legitimate email is marked as spam (false positive), the user might miss important communication.
Conclusion
In summary, precision is a measure that helps evaluate the effectiveness of a classification model by considering True Positives and False Positives, making option 'B' the right choice.
Precision is a crucial evaluation metric in classification problems, particularly in contexts like medical diagnoses or spam detection, where false positives can lead to significant issues.
What is Precision?
Precision quantifies the accuracy of the positive predictions made by a model. It reflects how many of the predicted positive cases were actually positive.
Formula for Precision
Precision is calculated using the formula:
Precision = True Positives / (True Positives + False Positives)
Key Components of Precision
- True Positives (TP): These are the cases where the model correctly predicts the positive class.
- False Positives (FP): These are the instances where the model incorrectly predicts the positive class when it is actually negative.
Why Option B is Correct?
The correct answer is option 'B' because:
- Precision specifically focuses on True Positives and False Positives.
- It answers the question: "Of all instances predicted as positive, how many are actually positive?"
Implications of False Positives
- High precision indicates a low rate of false positives, which is essential in scenarios where false alarms can lead to unnecessary actions or anxiety.
- For example, in spam detection, if a legitimate email is marked as spam (false positive), the user might miss important communication.
Conclusion
In summary, precision is a measure that helps evaluate the effectiveness of a classification model by considering True Positives and False Positives, making option 'B' the right choice.
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What does the precision evaluation metric primarily take into account?a)True Positives and False Negativesb)True Positives and False Positivesc)True Negatives and False Positivesd)True Negatives and False NegativesCorrect answer is option 'B'. Can you explain this answer?
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