What aspect does using heuristic shortcuts in multi-way splits complicate?

Using heuristic shortcuts in multi-way splits complicates the process of split search. In the context of decision trees, split search refers to the method of identifying the optimal points to partition the dataset into distinct groups based on predictor variables. Heuristic shortcuts are strategies employed to simplify the computational complexity of this search process. However, these shortcuts can lead to suboptimal splits or missed opportunities to find the best partitioning points, ultimately complicating the effectiveness and accuracy of the split search process.

In contrast, consensus prediction, tree interpretation, and variable selection are more about the outcomes or applications of the splits rather than the mechanics of how those splits are determined. Consensus prediction relates to how multiple trees or models can combine their outputs, tree interpretation involves understanding and explaining the structure and decisions made by a tree, and variable selection focuses on the process of determining which predictor variables should be included in the model. While all these aspects are vital in the modeling process, the direct complications from heuristic shortcuts specifically manifest during the split search phase.