Spark uses a single decision tree model to process the entire dataset, which may lead to memory issues and slow performance.

Spark distributes the dataset across multiple nodes and builds a single decision tree model in parallel, but does not support any further optimizations.

Spark uses a distributed version of the decision tree algorithm, where each node in the cluster builds a separate decision tree and the final model is a combination of these individual trees.

Spark uses a distributed version of the decision tree algorithm, where each node in the cluster builds a separate decision tree, and the final model is an ensemble of these individual trees, such as a random forest or gradient boosted trees.