Batched Lazy Decision Trees
This work addresses efficiency issues in decision tree-based classification for machine learning practitioners, but it is incremental as it builds on existing lazy and eager tree methods.
The paper tackles the problem of inefficient node visits in decision tree predictions by introducing a batched lazy algorithm that reduces computation time and memory usage while maintaining accuracy, achieving improvements in both metrics compared to conventional and lazy decision tree methods.
We introduce a batched lazy algorithm for supervised classification using decision trees. It avoids unnecessary visits to irrelevant nodes when it is used to make predictions with either eagerly or lazily trained decision trees. A set of experiments demonstrate that the proposed algorithm can outperform both the conventional and lazy decision tree algorithms in terms of computation time as well as memory consumption, without compromising accuracy.