Random forests are one of the most popular machine learning methods due to their accuracy and variable importance assessment. However, random only provide in a global sense. There is an increasing need for such assessments at local level, motivated by applications personalized medicine, policy-making, bioinformatics. We propose new nonparametric estimator that pairs flexible forest kernel with ...

As a testament to their success, the theory of random forests has long been outpaced by their application in practice. In this paper, we take a step towards narrowing this gap by providing a consistency result for online random forests.

The random forests method is one of the most successful ensemble methods. However, random forests do not have high performance when dealing with very-high-dimensional data in presence of dependencies. In this case one can expect that there exist many combinations between the variables and unfortunately the usual random forests method does not effectively exploit this situation. We here investig...

In Random Forests [2] several trees are constructed from bootstrapor subsamples of the original data. Random Forests have become very popular, e.g., in the fields of genetics and bioinformatics, because they can deal with high-dimensional problems including complex interaction effects. Conditional Inference Forests [8] provide an implementation of Random Forests with unbiased variable selection...

Abstract Black box machine learning models are currently being used for high-stakes decision making in various parts of society such as healthcare and criminal justice. While tree-based ensemble methods random forests typically outperform deep on tabular data sets, their built-in variable importance algorithms known to be strongly biased toward high-entropy features. It was recently shown that ...

Random Forests have been shown to perform very well in propositional learning. FORF is an upgrade of Random Forests for relational data. In this paper we investigate shortcomings of FORF and propose an alternative algorithm, RF, for generating Random Forests over relational data. RF employs randomly generated relational rules as fully self-contained Boolean tests inside each node in a tree and ...