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confusion matrix exam question solve | confusion matrix A to Z | precision , recall,accura formula

confusion matrix exam question solve | confusion matrix A to Z | precision , recall,accura formula
#engineermrsubir

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Link- https://youtube.com/playlist?list=PLJ-h-EbbnF3MmVOZCF2azDOJpOctwnk0M

Whether you're a student cramming for an exam, a professional refreshing your knowledge, or simply an enthusiast seeking to deepen your understanding, this curated playlist is designed to provide you with essential concepts, practical applications, and insightful discussions in the field of AI and ML. Here, you will find all exam question solutions, go through the playlist
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Let's define the terms:

True Positive (TP): Spam emails correctly identified as spam.
False Negative (FN): Spam emails incorrectly identified as not spam.
False Positive (FP): Non-spam emails incorrectly identified as spam.
True Negative (TN): Non-spam emails correctly identified as not spam.
Calculations
TP (True Positive): Spam emails correctly identified as spam.

Given: Out of 150 detected as spam, only 50 are actually spam.
So, TP = 50.
FN (False Negative): Spam emails incorrectly identified as not spam.

Total spam emails = 200.
TP (correctly identified as spam) = 50.
FN = Total spam - TP = 200 − 50 = 150
200−50=150.
FP (False Positive): Non-spam emails incorrectly identified as spam.

Total detected as spam = 150.
True Positives (actual spams identified correctly) = 50.
FP = Total detected as spam - TP = 150−50=100
150−50=100.
TN (True Negative): Non-spam emails correctly identified as not spam.

Total emails = 10,000.
Total non-spam = Total emails - Total spam = 10,000−200=9,
800
10,000−200=9,800.
FP = 100.
TN = Total non-spam - FP = 9,800−100=9,700
9,800−100=9,700.
Summary
TP = 50
FN = 150
FP = 100
TN = 9,700
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