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?

Simulated

L1 regularization, which adds a penalty term to the loss function based on the absolute values of the model parameters.

8.5%

L2 regularization, which adds a penalty term to the loss function based on the squared values of the model parameters.

9.3%

Both A and B, as Spark MLlib supports both L1 and L2 regularization techniques for linear regression models.

78.8%

None of the above, as regularization techniques are not applicable to linear regression models in Spark MLlib.

3.4%

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