Boosted trees, such as those used in the XGBoost algorithm, are a powerful ensemble learning method.
However, a key limitation of boosted trees is that they cannot be fit in parallel due to their sequential nature.
Sequential Ensemble Method
Boosted trees work by combining multiple decision trees in a sequential manner. Each tree is trained on the errors of the previous trees, with the goal of correcting those mistakes. This iterative process allows the ensemble to gradually improve its performance by focusing on the most challenging examples.
The sequential nature of boosted trees is a fundamental aspect of the algorithm. It enables each subsequent tree to learn from the mistakes of its predecessors, ultimately creating a strong predictive model.
Dependency on Previous Trees
In the boosting process, each tree depends on the results of the trees that came before it. The input data for a given tree is weighted based on the errors of the previous trees. Examples that were misclassified by earlier trees are given higher weights, forcing the current tree to focus on these difficult cases.
This dependency on previous results means that the trees must be fit sequentially. The input data for each tree can only be determined after the previous trees have been trained and their errors have been calculated. Attempting to fit the trees in parallel would break this core principle of boosting.
Limitations on Parallelization
While the overall process of fitting boosted trees must be sequential, certain parts of the algorithm can still be parallelized.
For example, finding the best split points within a single tree can be done in parallel, as it does not depend on the results of other trees.
However, fully parallelizing the boosted tree algorithm would require breaking the sequential dependency between trees. This would fundamentally alter the nature of the algorithm and likely degrade its performance.