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Answer: Expand the dataset with more training examples, Decrease the number of features used in the model, Apply stronger regularization to the model
To combat overfitting in your spam classifier, consider these three strategies: - **Expand the dataset with more training examples (A)**: More data can help the model learn more general patterns rather than memorizing the training set. - **Decrease the number of features used in the model (B)**: Simplifying the model by reducing features can prevent it from capturing noise in the training data. - **Apply stronger regularization to the model (E)**: Increasing regularization penalizes complexity, encouraging the model to focus on the most significant patterns. These approaches aim to improve the model's ability to generalize to unseen data by reducing overfitting.
Author: LeetQuiz Editorial Team
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While training a spam classifier, you observe that the model is overfitting the training data. Which of the following strategies can help mitigate this issue? (Select three.)
A
Expand the dataset with more training examples
B
Decrease the number of features used in the model
C
Enhance the model's complexity by adding more features
D
Reduce the size of the training dataset
E
Apply stronger regularization to the model
F
Weaken the regularization parameters
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