How does Naïve Bayes behave in distributed parameter estimation systems at scale?

Naïve Bayes is highly efficient in distributed systems because its parameters are additive sufficient statistics.

In distributed environments like Spark or Hadoop, each node computes local feature counts and class priors. These are then aggregated globally using simple summation, making NB embarrassingly parallel. This avoids gradient synchronization or iterative optimization, making NB extremely scalable for big data systems.