Automodel learn/test
Is there a good reason to split the data in 60/20/20% where the last 20% is used to "test the testing of the conclusion", as proposed in another platform?
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Hello @DocMusher
Looks similar to RM automodel, where the data is split into 60:40 (train: test) but the 40% test data is again split into 7 hold out sets to test the model.
Looks similar to RM automodel, where the data is split into 60:40 (train: test) but the 40% test data is again split into 7 hold out sets to test the model.
We split the 40% again into 7 parts, evaluate the model on each part, get rid of the two extremes/outliers, and build the average of the rest. This way we keep many of the benefits of a cross-validation without it's biggest drawback: 5x-10x runtime increases. (Explanation from @IngoRM)
@IngoRM might add more here.
Looks similar to RM automodel, where the data is split into 60:40 (train: test) but the 40% test data is again split into 7 hold out sets to test the model.