Thakshashila: /* SEO Keywords */

2025-06-10T06:23:55Z

SEO Keywords

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	precision in machine learning, positive predictive value, classification metric, ML model accuracy, spam detection precision, precision formula		precision in machine learning, positive predictive value, classification metric, ML model accuracy, spam detection precision, precision formula
			[[Category:Artificial Intelligence]]

Thakshashila: Created page with "= Precision = '''Precision''' is a metric used in classification tasks to measure how many of the predicted positive results are actually correct. It is also known as the '''positive predictive value'''. == Definition == : $\text{Precision} = \frac{TP}{TP + FP}$ Where: * '''TP''' = True Positives – correct positive predictions * '''FP''' = False Positives – incorrect positive predictions Precision helps to answer the question: '''"Of all the items la..."

2025-06-10T05:18:21Z

Created page with "= Precision = '''Precision''' is a metric used in classification tasks to measure how many of the predicted positive results are actually correct. It is also known as the '''positive predictive value'''. == Definition == :<math> \text{Precision} = \frac{TP}{TP + FP} </math> Where: * '''TP''' = True Positives – correct positive predictions * '''FP''' = False Positives – incorrect positive predictions Precision helps to answer the question: '''"Of all the items la..."

New page

= Precision =

'''Precision''' is a metric used in classification tasks to measure how many of the predicted positive results are actually correct. It is also known as the '''positive predictive value'''.

== Definition ==

:<math> \text{Precision} = \frac{TP}{TP + FP} </math>

Where:
* '''TP''' = True Positives – correct positive predictions
* '''FP''' = False Positives – incorrect positive predictions

Precision helps to answer the question: '''"Of all the items labeled as positive, how many are truly positive?"'''

== Simple Example ==

Imagine a spam filter that marked 100 emails as spam. Out of these, 80 were actually spam, and 20 were not.

* TP = 80
* FP = 20

Then,

:<math> \text{Precision} = \frac{80}{80 + 20} = \frac{80}{100} = 0.8 = 80\% </math>

This means that 80% of emails flagged as spam were truly spam.

== When to Use Precision ==

Precision is especially important when the cost of false positives is high.

=== Real-World Scenarios ===

* Medical testing: Avoiding telling a healthy person they are sick.
* Email spam detection: Ensuring important emails aren't marked as spam.
* Search engines: Returning highly relevant search results.

== High vs Low Precision ==

* '''High Precision''': Most positive predictions are correct.
* '''Low Precision''': Many false alarms (false positives).

== Related Metrics ==

* [[Recall]]
* [[F1 Score]]
* [[Accuracy]]
* [[Confusion Matrix]]

== SEO Keywords ==

precision in machine learning, positive predictive value, classification metric, ML model accuracy, spam detection precision, precision formula

Precision - Revision history

Thakshashila: /* SEO Keywords */