How does bagging affect decision tree models?

ensemble_methods
bagging
decision_trees

#1

I am currently studying about bagging and I know that It can decrease the variance of model by generating additional data for training from your original data set using combinations with repetitions to produce multisets of the same size as your original data. Can you help me to understand, how does it help in classification methods specifically in Decision Tree.

Thanks,
Sid


#2

@sid100158
Bagging helps in reducing the normalization effect by this following characteristic
-Not every predictor sees each data point
-lowers “complexity” of the overall average
-Usually,better generalization performance