Spark uses a single node to process all data and performs linear regression sequentially.

Spark distributes the data across multiple nodes and uses parallel processing to compute the regression coefficients, leveraging techniques like gradient descent optimization and iterative algorithms to handle large datasets efficiently.

Spark only supports decision trees and does not scale linear regression.

Spark uses a centralized server to collect all data and then processes it using linear regression.