Bootstrapping after training_test split or original dataset?
Bootstrapping from the original dataset defeats the purpose of having the two different datasets (train/test). You always train the model on the training dataset and calculate the accuracy or another score on the test dataset.
If you bootstrap from the original dataset, you'll be training your model on (a subset of) the whole dataset, which will include a part of the test dataset, so your resulting metrics (that you'll calculate based on the test dataset) will be biased because you trained the model on part of the test dataset.
My dataset contains 1000 sets which I'm using it for the classification problem. I used LR and SVM as a machine learning algorithm. I was wondering if I should use Bootstrapping from training set and then apply LR & SVM or use Bootstrapping from original dataset? When I look at the accuracy score, I shouldn't use bootstrapping from original dataset.