Here is how I automatically store all (49) learners from sk:
using AutoMLPipeline sk= AutoMLPipeline.SKLearners.learner_dict; sk= keys(sk) |> collect |> x-> sort(x,lt=(x,y)->lowercase(x)<lowercase(y))
I realize some of these learners apply to regression others classification, but let’s put that aside for a moment.
Suppose I want to train all models in “sk”
learners = DataFrame() for m in skv learner = SKLearner(m) pcmc = AutoMLPipeline.@pipeline learner println(learner.name) mean,sd,_ = crossvalidate(pcmc,X,y,"accuracy_score",10) global learners = vcat(learners,DataFrame(name=learner.name,mean=mean,sd=sd)) end; @show learners;
It appears that the performance metrics in your package currently work for classification and not regression (such as RMSE) unless I’m missing something?