User 2568 | 3/19/2016, 3:20:25 AM
Having used modelparametersearch extensively over the last few days, I found the idea of accepting the dataset as SFrame | KFold | tuple worked really well. Its simple to understand and very expressive from a computation language perspective.
I propose the model create methods work the same way. This has the following benefits: 1. the model create methods work the same as the parameter search methods. 2. I can write BoostedTreesClassifier.classify((train, validate) ....) which is a nice shorthand for BoostedTreesClassifier.classify(train, validation_set=validate ....) . The short hand is perhaps more natural as I can now write the following which is a very elegant description of the computation:
dataset = train_data.random_split(0.8) model = BoostedTreesClassifier.classify(dataset, ....)
For kfold cross validation, I can write the following, which is simple and clear:
kfold = traindata.kfold(5) modellist = BoostedTreesClassifier.classify(kfold ....)
which returns a list of models.
What do you think?