In the context of Spark MLlib, explain how Spark scales linear regression for large datasets and what are the key components involved in this process.

Simulated

Spark uses a single machine to process the entire dataset, applying a standard linear regression algorithm.

12.9%

Spark distributes the data across multiple nodes, parallelizing the computation of the cost function and gradient descent steps.

58.6%

Spark applies a distributed version of the stochastic gradient descent algorithm, which updates the model parameters iteratively using subsets of the data.

19.9%

Spark uses a combination of distributed data storage and a parallelized version of the normal equation to solve for the model parameters.

8.6%

Databricks Certified Machine Learning - Associate

Get started today

Comments

In the context of Spark MLlib, explain how Spark scales linear regression for large datasets and what are the key components involved in this process.