In a Spark MLlib implementation, you are tasked with optimizing the performance of a linear regression model on a large dataset. Which of the following strategies would be most effective in improving the scalability and efficiency of the model?

Simulated

Increase the number of iterations of the gradient descent algorithm to ensure convergence.

9.4%

Use a distributed version of the stochastic gradient descent algorithm to parallelize the computation.

62.9%

Reduce the number of features in the dataset to simplify the model and reduce the computational complexity.

17.0%

Increase the batch size of the gradient descent algorithm to speed up the convergence.

10.7%

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