I have some ambiguity about dividing training dataset in Bagging tree.
In fact I have found in this article About Decision Tree Ensembles- Bagging
That : the idea is to create several subsets of data from training sample chosen randomly with replacement.
I want to understand which of the next solutions is bagging technique: if the total dataset is D (Without the target value y)
Splitting D to X_train and X_test :
Splitting X_train to sevral subtraining sets like : X_train1, X_train2, X_train3, X_train4 etc. where X_train1 + X_train2 +X_train3+X_train4 +....=X_train.
Finally training each dataset separatly and generating different model for each trained dataset.
Average of all the predictions (generated trees) is used to get the unique final output tree
- Splitting D to X_train1 and X_test1 :
- Training the dataset and generating the first decision tree
- Splitting D to X_train2 and X_test2
- Training the dataset and generating the second decision tree
- Average of all the predictions (generated trees) is used to get the unique final output tree