Supervised Learning

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Example Scenario: Email Spam Detection

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Consider an email service provider implementing a spam detection system. The goal is to

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emails as either “spam” or “not spam” (also known as “ham”).

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Precision:

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Precision measures the proportion of corectly identified spam emails out of all emails

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by the model.

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Formula: Precision = True Positives / (True Positives + False Positives)

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True Positives (TP): The number of emails correctly classified as spam.

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False Positives (FP): The number of non-spam emails incorrectly classified as

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Precision Example: Let’s say the spam detection system flagged 100 emails as Sp

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inspection

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90 of them were actually spam (True Positives)

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Precision = 90 /(90 + 10) = 90%

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So

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in this scenario

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flagged as spam

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90% of them were actually spam

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Recall:

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Recall measures the proportion of correctly identified spam emails out of all actual

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dataset.

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Formula: Recall =True Positives / (True Positives + False Negatives)

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False Negatives (FN): The number of spam emails incorrectly classified as no

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Recall Example: Suppose there were I 50 actual spam emails in the dataset. The spa

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correctly identified 90 of them as spam (True Positives)

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but it missed 60 spam emails

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Recall = 90/(90 + 60) = 60%

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In this case. the recall ofthe spam detection system is 60%. This indicates that out of all

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the system managed to identify 60% of them correctly

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but it missed 40%.

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Interpretation:

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• High Precision: Indicates that when the model preuicts an email as spam

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it 19

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correct. This reduces the chances of legitimate emails being incorrectly marke

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crucial for user trust and satisfaction