Being able to parallelize
Classify would be useful on multiple fronts:
Performance-wise such parallelization would enable the use of larger datasets and HPC resources; Streaming-wise parallelization would enable certain real-time, distributed, cloud applications; Pedgagogically-wise, parallelization could enhance insights into the structure and judicious choice of predicting/classifying
Methods while Scientifically, fine-grained, customized parallelization could promote reproducibility, training variations, extensions of built-in classifiers and the creation of new, hybrid methods.
So are there good frameworks/strategies for parallelizing