Modelling species presence‐only data with random forests

Analysis of missing data with random forests

Random Forests for Big Data

Big Data is one of the major challenges of statistical science and has numerous consequences from algorithmic and theoretical viewpoints. Big Data always involve massive data but they also often include data streams and data heterogeneity. Recently some statistical methods have been adapted to process Big Data, like linear regression models, clustering methods and bootstrapping schemes. Based o...

Random survival forests for high-dimensional data

Minimal depth is a dimensionless order statistic that measures the predictiveness of a variable in a survival tree. It can be used to select variables in high-dimensional problems using Random Survival Forests (RSF), a new extension of Breiman’s Random Forests (RF) to survival settings. We review this methodology and demonstrate its use in high-dimensional survival problems using a public domai...

Functional Data Classification with Kernel-Induced Random Forests

Scientists and others today often collect samples of curves and other functional data. The multivariate data classification methods cannot be directly used for functional data classification because the curse of dimensionality and difficulty in taking in account the correlation and order of functional data. We extend the kernel-induced random forest method for discriminating functional data by ...

Exploratory Data Analysis using Random Forests

Although the rise of "big data" has made machine learning algorithms more visible and relevant for social scientists, they are still widely considered to be "black box" models that are not well suited for substantive research: only prediction. We argue that this need not be the case, and present one method, Random Forests, with an emphasis on its practical application for exploratory analysis a...