Decision trees are trained sequentially on a single node without any partitioning or parallel processing.

Data is partitioned across multiple nodes, and parallel processing is used to train decision trees efficiently, with Spark's MLlib providing optimized algorithms for feature selection and node splitting.

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

Decision trees are trained on a centralized server without any partitioning or parallel processing.