Training Big Random Forests with Little Resources
This work addresses the challenge of training big random forests without large compute clusters, which is significant for users with limited resources, though it appears incremental as it builds on existing random forest methods with a novel implementation.
The paper tackles the problem of building random forests on large datasets with limited computational resources by proposing a multi-level construction scheme that efficiently constructs ensembles of huge trees for hundreds of millions or billions of training instances using commodity hardware, demonstrating practical merits on dense datasets.
Without access to large compute clusters, building random forests on large datasets is still a challenging problem. This is, in particular, the case if fully-grown trees are desired. We propose a simple yet effective framework that allows to efficiently construct ensembles of huge trees for hundreds of millions or even billions of training instances using a cheap desktop computer with commodity hardware. The basic idea is to consider a multi-level construction scheme, which builds top trees for small random subsets of the available data and which subsequently distributes all training instances to the top trees' leaves for further processing. While being conceptually simple, the overall efficiency crucially depends on the particular implementation of the different phases. The practical merits of our approach are demonstrated using dense datasets with hundreds of millions of training instances.