In the Hands-On ML with Scikit-Learn book, it states that,
...bagging ends up with a slightly higher bias than pasting, but... the ensemble's variance is reduced.
I am a bit confused about this part. Wouldn't bagging have higher variance and lower bias, since the sampled instances will be more correlated with each other compared to pasting? (Similar to how leave-one-out CV has higher variance due to higher correlation compared to K-fold.)
Or, is it just because bagging can sample more instances and train higher number of predictors compared to pasting? But in this case, bagging will have lower variance but not necessarily higher bias?