Thakshashila: /* SEO Keywords */

2025-06-10T06:24:27Z

SEO Keywords

← Older revision		Revision as of 06:24, 10 June 2025
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	recall in machine learning, true positive rate, sensitivity, recall formula, classification evaluation, medical test recall, fraud detection model		recall in machine learning, true positive rate, sensitivity, recall formula, classification evaluation, medical test recall, fraud detection model
			[[Category:Artificial Intelligence]]

Thakshashila: /* Related Metrics */

2025-06-10T05:18:54Z

Related Metrics

← Older revision		Revision as of 05:18, 10 June 2025
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	* [[F1 Score]]		* [[F1 Score]]
	* [[Confusion Matrix]]		* [[Confusion Matrix]]
	* [[Sensitivity and Specificity]]		* [[Sensitivity]] and [[Specificity]]

	== SEO Keywords ==		== SEO Keywords ==

	recall in machine learning, true positive rate, sensitivity, recall formula, classification evaluation, medical test recall, fraud detection model		recall in machine learning, true positive rate, sensitivity, recall formula, classification evaluation, medical test recall, fraud detection model

Thakshashila: Created page with "= Recall = '''Recall''' is a metric used in classification to measure how many of the actual positive instances were correctly identified by the model. It is also known as '''sensitivity''' or the '''true positive rate'''. == Definition == : $\text{Recall} = \frac{TP}{TP + FN}$ Where: * '''TP''' = True Positives – correctly predicted positive instances * '''FN''' = False Negatives – actual positives incorrectly predicted as negative Recall answers th..."

2025-06-10T05:18:31Z

Created page with "= Recall = '''Recall''' is a metric used in classification to measure how many of the actual positive instances were correctly identified by the model. It is also known as '''sensitivity''' or the '''true positive rate'''. == Definition == :<math> \text{Recall} = \frac{TP}{TP + FN} </math> Where: * '''TP''' = True Positives – correctly predicted positive instances * '''FN''' = False Negatives – actual positives incorrectly predicted as negative Recall answers th..."

New page

= Recall =

'''Recall''' is a metric used in classification to measure how many of the actual positive instances were correctly identified by the model. It is also known as '''sensitivity''' or the '''true positive rate'''.

== Definition ==

:<math> \text{Recall} = \frac{TP}{TP + FN} </math>

Where:
* '''TP''' = True Positives – correctly predicted positive instances
* '''FN''' = False Negatives – actual positives incorrectly predicted as negative

Recall answers the question: '''"Of all actual positive cases, how many did we correctly identify?"'''

== Simple Example ==

A medical test is used to detect cancer. There are 100 people with cancer:

* The test correctly identifies 90 as having cancer (TP = 90)
* It misses 10 people (FN = 10)

:<math> \text{Recall} = \frac{90}{90 + 10} = \frac{90}{100} = 0.9 = 90\% </math>

This means the test correctly detects 90% of cancer cases.

== When to Use Recall ==

Recall is crucial when missing a positive case has serious consequences.

=== Real-World Scenarios ===

* Cancer diagnosis: Missing a sick patient (false negative) is risky.
* Fraud detection: It's better to catch all suspicious activity even if some are false alarms.
* Fire alarms: Better to alert even for minor smoke than miss a real fire.

== High vs Low Recall ==

* '''High Recall''': Most actual positives are identified.
* '''Low Recall''': Many positives are missed (false negatives).

== Related Metrics ==

* [[Precision]]
* [[F1 Score]]
* [[Confusion Matrix]]
* [[Sensitivity and Specificity]]

== SEO Keywords ==

recall in machine learning, true positive rate, sensitivity, recall formula, classification evaluation, medical test recall, fraud detection model

Recall - Revision history

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