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Reasoning: I'm currently co-teaching a course where students can do the exercises in R using MLR3 or Python using scikit-learn. Doing the exercises in R appears to be less repetitive overall, as for example, there is a simple function for benchmarking. Also, it would require less time to actually wait for the results to finish as one could make more use of parallelism.
The text was updated successfully, but these errors were encountered:
You can already simply do that by creating your Pipeline objects, and passing them to a cross_val_score in a look over your datasets and pipelines. Since the code snippet required to do this is just a few lines, I don't think it makes sense for us to add it to the public API.
Describe the workflow you want to enable
I would like to define multiple pipelines and compare them against each other on multiple datasets.
Describe your proposed solution
A single helper function that executes this benchmark fully in parallel. This would allow
Describe alternatives you've considered, if relevant
There is an MLR3 function that inspired this issue.
Additional context
Reasoning: I'm currently co-teaching a course where students can do the exercises in R using MLR3 or Python using scikit-learn. Doing the exercises in R appears to be less repetitive overall, as for example, there is a simple function for benchmarking. Also, it would require less time to actually wait for the results to finish as one could make more use of parallelism.
The text was updated successfully, but these errors were encountered: