AWS Certified Cloud Practitioner
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What is the main goal of regularization in ML models?
Real Exam
Community
RRitesh
A
To increase the number of layers
B
To prevent overfitting by penalizing model complexity
C
To reduce inference time
D
To eliminate all bias
Explanation:
Regularization in machine learning is a technique used to prevent overfitting by adding a penalty term to the loss function that discourages overly complex models. This helps the model generalize better to unseen data by controlling model complexity.
Key points about regularization:
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Overfitting prevention: The primary goal is to prevent models from fitting too closely to training data noise
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Complexity penalty: Regularization adds a penalty term (like L1/L2 norms) to the loss function
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Generalization: Helps models perform better on new, unseen data
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Common techniques: L1 regularization (Lasso), L2 regularization (Ridge), and Elastic Net
Why other options are incorrect:
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A: Increasing layers is about model architecture, not regularization
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C: Reducing inference time is about optimization, not regularization's primary goal
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D: Eliminating all bias is impossible and not the goal of regularization
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