search

PyDataBlog / ParallelKMeans.jl

Parallel & lightning fast implementation of available classic and contemporary variants of the KMeans clustering algorithm
MIT License
50 stars 13 forks source link

Extend [compat] MLJModelInterface = "^0.2,^0.3" #89

Closed ablaom closed 4 years ago

ablaom commented 4 years ago

Supervised classifiers in MLJ now require a lower bound of 0.3 to buy into a performance boost. So you may find users complaining that adding ParallelKMeans to their MLJ environment downgrades performance of other models unless you update.

There should be nothing breaking about this update.

Questions welcome.

Arkoniak commented 4 years ago

Thank you for noticing, we've merged Compat PR, but forget to bump version.