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XGBoost uses the method of additive training in which it models the residual of the previous model.
This is sequential though, how does it to parallel computing then?
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c) XGBoost: XGBoost is an implementation of GBM, with major improvements. GBM's build trees sequentially, but XGBoost is parallelized. This makes XGBoost faster.
The training proceeds iteratively, adding new trees that predict the residuals or errors of prior trees that are then combined with previous trees to make the final prediction. It's called gradient boosting because it uses a gradient descent algorithm to minimize the loss when adding new models.
Random forest learning is implemented using C in MPI. By using parallel methods, we can improves the accuracy of the classificagon using less gme. We can apply this parallel methods on larger dataset and try to parallelize the construcgon for each decision tree.
Both xgboost and gbm follows the principle of gradient boosting. There are however, the difference in modeling details. Specifically, xgboost used a more regularized model formalization to control over-fitting, which gives it better performance.
Parallelization – The process of sequential tree building is done using the parallelized implementation in the XGBoost algorithm. This is made possible due to the outer and inner loops that are interchangeable.
The XGBoost is having a tree learning algorithm as well as linear model learning, and because of that, it is able to do parallel computation on a single machine. This makes it 10 times faster than any of the existing gradient boosting algorithms.
XGBoost has a distributed weighted quantile sketch algorithm to effectively handle weighted data. Sparsity-aware Split Finding: In many real-world problems, it is quite common for the input x to be sparse. There are multiple possible causes for sparsity:
Tree Pruning – The XGBoost algorithm uses the depth-first approach, unlike the stopping criterion for tree splitting used by GBMS, which is greedy in nature and it also depends upon the negative loss criterion. The XGBoost instead uses the max depth feature/parameter, and hence it prunes the tree in a backward direction.
Xgboost doesn't 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. You will see all your cores firing on one tree. This is why it's so fast- well engineered.
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