I am studying the ensemble machine learning and when I read some articles online, I encountered 2 questions.
In this article, it mentions
Instead, model 2 may have a better overall performance on all the data points, but it has worse performance on the very set of points where model 1 is better. The idea is to combine these two models where they perform the best. This is why creating out-of-sample predictions have a higher chance of capturing distinct regions where each model performs the best.
But I still cannot get the point, why not train all training data can avoid the problem?
From this article, in the prediction section, it mentions
Simply, for a given input data point, all we need to do is to pass it through the M base-learners and get M number of predictions, and send those M predictions through the meta-learner as inputs
But in the training process, we use k -fold train data to train M base-learner, so should I also train M base-learner based on all train data for the input to predict?