Random forest (RF) methodology is a nonparametric methodology for prediction problems. A standard way to use RFs includes generating a global RF to predict all test cases of interest. In this article, ...

As a result of this joint learning, splits in the random forest are more likely to occur along informative genetic features that are orthogonal (that is, not correlated) to population structure.

A key observation is that these properties are closely related to the relevance and exclusion requirements of valid instrumental variables. We design a data-driven procedure to select tuples of ...

Relative importance of regressor variables is an old topic that still awaits a satisfactory solution. When interest is in attributing importance in linear regression, averaging over orderings methods ...