MinSizeKmeans VS SeaLion

Sure so basically I scraped the volume from the SEC report figure depicting 'Buy' volume by measuring pixels between ticks on the y axis. I also downloaded all regular session trades for the dates in that figure and after some rounds of data cleaning (ie one hot encoding trade condition data) I ran the trades from the first half through an implementation of kmeans clustering with minimum cluster size constraints set to cluster the trades weighted by volume into 2 groups with minimum weight of the volume scraped from the candle from the SEC report. This clustering takes a long time to run so I've only managed to process that first bar.

I was searching reddit earlier, for certain machine learning topics, and I came across a topic which looks like a showcase. By following the links leading to the repo, I found some .pyx and .pxd hybrid modules. I was always skeptical about taking the trouble of writing modules in this weird syntax, expecting promising performance gains. By searching cython projects on github, I found many others. It looks like some people found it interesting to adopt in their projects. What can you consider as valid use case(s) for cython? I mean if you're really that worried about performance, which you can't get using python, wouldn't it be wiser to use optimized C/C++ with possibly a python API?

We do that already inside of the source code. The ensemble learning classifier has a method in which you can train multiple models all at once in parallel and then get the best classifier on the dataset. You can check out the ensemble learning tutorials here : ensemble learning tutorial