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Answer: Increase the number of training examples, Use a reduced set of features, Increase the value of regularization parameters
To combat overfitting in a spam classifier, consider these strategies: - **Increase the number of training examples**: More data helps the model generalize better by exposing it to a wider variety of examples. - **Use a reduced set of features**: Simplifying the model by focusing on fewer features can prevent it from learning noise in the data. - **Increase the value of regularization parameters**: Techniques like L1 or L2 regularization penalize overly complex models, encouraging simplicity and reducing overfitting.
Author: LeetQuiz Editorial Team
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Which of the following strategies can help mitigate overfitting when training a spam classifier? (Select three correct options.)
A
Use an expanded set of features
B
Increase the number of training examples
C
Decrease the value of regularization parameter
D
Use a reduced set of features
E
Increase the value of regularization parameters
F
Decrease the number of training examples
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