Comments (1)
The stacking estimator is defined here: https://github.com/EpistasisLab/tpot/blob/master/tpot/builtins/stacking_estimator.py
effectively, what it does is takes the predictions of the model and appends it to the left of the inputted data X. If its a classifier with predict_proba, the all class probabilities are also included. If you have a binary class, that means that there would be two additional columns, one for each class.
so in your case trans_x_t is [model 1 predicted labels, model 1 probability for class 0, model 1 probability for class 1, ]
similarly
trans_x_t1 would be [model 2 predicted labels, model 2 probability for class 0, model 2 probability for class 1, <trans_x_t>]
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