I am new to Data Science and have been studying the methods of stacking to find out if it can meet the following fact, but I did not find or understand evidence that it can or cannot work.
Let's imagine a dataset divided into two folds (train and test). For the Layer 1 models, the only data I can get is the predictions from the test fold. Would stacking principles work using those predictions as a feature, or could the fact that each Layer 1 model is using and predicting the same fold observations cause problems like overfitting?
I ask this question because I have seen that almost every time people talk about stacking, mechanisms like k-fold cross-validation are applied, but I can't get the predictions for those mechanisms, only the ones made for the test set.