Under any given trained model, if I wanted to compute the predictions of the training dataset, D.R would perform something called Stacked Predictions.
I'm wondering if Stacked Predictions is just a fancy way of saying Cross Validation. Is this a right assumption?
Because when you do click "Compute Predictions", on the right hand side, I only saw one model being used by D.R to compute the predictions. But the problem i am seeing is that in order for D.R to give you out-of-sample predictions for all training data, it would have to use the partition-specific model, which as far as it's concerned is an out-of-sample partition, to compute the predictions for the rows corresponding to that specific partition.
Interestingly, if i upload a dataset and change the initial partition method to TVH, rather than CV, and compute the predictions for all traning data using a trained model, it somehow still comes up with partitions.
So this got me wondering if it is re-partitioning and re-training multiple models on these various partitions behind the scenes, even after model-tranining is done. Any ideas on whether this is the case?
Any clarifications are appreciated!