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Answer: Both A and B, as Spark MLlib supports both L1 and L2 regularization techniques for linear regression models.
In a Spark MLlib implementation, both L1 and L2 regularization techniques can be applied to linear regression models to prevent overfitting and improve their generalization performance. L1 regularization adds a penalty term to the loss function based on the absolute values of the model parameters, promoting sparsity in the model coefficients. L2 regularization adds a penalty term based on the squared values of the model parameters, encouraging smaller coefficients. Spark MLlib supports both L1 and L2 regularization techniques, allowing users to choose the appropriate regularization method based on their specific requirements and dataset characteristics.
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In a Spark MLlib implementation, you are working with a large dataset and need to build a linear regression model. Which of the following regularization techniques can be applied to the model to prevent overfitting and improve its generalization performance?
A
L1 regularization, which adds a penalty term to the loss function based on the absolute values of the model parameters.
B
L2 regularization, which adds a penalty term to the loss function based on the squared values of the model parameters.
C
Both A and B, as Spark MLlib supports both L1 and L2 regularization techniques for linear regression models.
D
None of the above, as regularization techniques are not applicable to linear regression models in Spark MLlib.