stackingstacking has the same idea soyou run multiple models on the datacombine the results together somehow the subtle difference here between bucket of models and stackingis that you pick the model that wins soyou' run train/testyou find the model that works best for your dataand you use that model by contraststacking will combine the results of all those models togetherto arrive at final result nowthere is whole field of research on ensemble learning that tries to find the optimal ways of doing ensemble learningand if you want to sound smartusually that involves using the word bayes lot sothere are some very advanced methods of doing ensemble learning but all of them have weak pointsand think this is yet another lesson in that we should always use the simplest technique that works well for us now these are all very complicated techniques that can' really get into in the scope of this bookbut at the end of the dayit' hard to outperform just the simple techniques that we've already talked about few of the complex techniques are listed herebayes optical classifierin theorythere' something called the bayes optimal classifier that will always be the bestbut it' impracticalbecause it' computationally prohibitive to do it bayesian parameter averagingmany people have tried to do variations of the bayes optimal classifier to make it more practicallike the bayesian parameter averaging variation but it' still susceptible to overfitting and it' often outperformed by baggingwhich is the same idea behind random forestsyou just resample the data multiple timesrun different modelsand let them all vote on the final result turns out that works just as welland it' heck of lot simplerbayesian model combinationfinallythere' something called bayesian model combination that tries to solve all the shortcomings of bayes optimal classifier and bayesian parameter averaging butat the end of the dayit doesn' do much better than just cross validating against the combination of models againthese are very complex techniques that are very difficult to use in practicewe're better off with the simpler ones that we've talked about in more detail butif you want to sound smart and use the word bayes lot it' good to be familiar with these techniques at leastand know what they are sothat' ensemble learning againthe takeaway is that the simple techniqueslike bootstrap aggregatingor baggingor boostingor stackingor bucket of modelsare usually the right choices there are some much fancier techniques out there but they're largely theoretical butat least you know about them now