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Apprendre Regularisation | Machine Learning Techniques
Data Anomaly Detection

bookRegularisation

Regularization is commonly employed when dealing with anomalies to mitigate their undue impact on predictive models. While regularization may not directly identify outliers, its primary role is to reduce the influence of outliers on the model's results.

Instead of explicitly detecting outliers, it focuses on making the model more robust and less sensitive to extreme data points.

Regularisation types

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How does L2 regularization (Ridge) impact a model's sensitivity to anomalies or outliers in the data?

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Section 3. Chapitre 3

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bookRegularisation

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Regularization is commonly employed when dealing with anomalies to mitigate their undue impact on predictive models. While regularization may not directly identify outliers, its primary role is to reduce the influence of outliers on the model's results.

Instead of explicitly detecting outliers, it focuses on making the model more robust and less sensitive to extreme data points.

Regularisation types

question mark

How does L2 regularization (Ridge) impact a model's sensitivity to anomalies or outliers in the data?

Select the correct answer

Tout était clair ?

Comment pouvons-nous l'améliorer ?

Merci pour vos commentaires !

Section 3. Chapitre 3
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