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So what I know about boosting technique, Like we train the data and update the weights of falsely predicted values or try to minimize the loss in the next model. So basically, it's the sequential process where we feed the output of one model to another.

In XGBoost it's said that model performs parallelly by Data parallelization or Model parallelization, so I'm not able to understand that if that's the case then how are we feeding out of first model or weak learner to next one if they are running parallelly on different nodes, isn't that similar to bagging or Random Forest Technique. I know I'm definitely wrong, but I'm not to get that how bagging technique is working parallely.

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Xgboost does not run multiple trees in parallel like you noted. You need predictions after each tree to update gradients. Rather, it does the parallelization WITHIN a single tree my using openMP to create branches independently. To observe this, build a giant dataset and run with n_rounds=1.

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    $\begingroup$ Without a link to the original content, this is plagiarism. $\endgroup$
    – Ben Reiniger
    Jul 29, 2022 at 17:03

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