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Selection: with tag predictor-selection [5 articles] 


Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics

Global Ecology and Biogeography, Vol. 27, No. 2. (February 2018), pp. 245-256,


[Aim] Species distribution modelling, a family of statistical methods that predicts species distributions from a set of occurrences and environmental predictors, is now routinely applied in many macroecological studies. However, the reliability of evaluation metrics usually employed to validate these models remains questioned. Moreover, the emergence of online databases of environmental variables with global coverage, especially climatic, has favoured the use of the same set of standard predictors. Unfortunately, the selection of variables is too rarely based on a careful examination of ...


Iterative random forests to discover predictive and stable high-order interactions

Proceedings of the National Academy of Sciences, Vol. 115, No. 8. (20 February 2018), pp. 1943-1948,


[Significance] We developed a predictive, stable, and interpretable tool: the iterative random forest algorithm (iRF). iRF discovers high-order interactions among biomolecules with the same order of computational cost as random forests. We demonstrate the efficacy of iRF by finding known and promising interactions among biomolecules, of up to fifth and sixth order, in two data examples in transcriptional regulation and alternative splicing. [Abstract] Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, ...


Classification and interaction in random forests

Proceedings of the National Academy of Sciences, Vol. 115, No. 8. (20 February 2018), pp. 1690-1692,


Suppose you are a physician with a patient whose complaint could arise from multiple diseases. To attain a specific diagnosis, you might ask yourself a series of yes/no questions depending on observed features describing the patient, such as clinical test results and reported symptoms. As some questions rule out certain diagnoses early on, each answer determines which question you ask next. With about a dozen features and extensive medical knowledge, you could create a simple flow chart to connect and order ...


Comparing and selecting spatial predictors using local criteria

Vol. 2013 (2013), 21-13


Remote sensing technology for the study of Earth and its environment has led to “Big Data” that, paradoxically, have global extent but may be spatially sparse. Furthermore, the variability in the measurement error and the latent process error may not fit conveniently into the Gaussian linear paradigm. In this paper, we consider the problem of selecting a predictor from a finite collection of spatial predictors of a spatial random process defined on D, a subset of d-dimensional Euclidean space. Critically, we ...


Local spatial-predictor selection

Vol. 2013 (2013), 09-13


Consider the problem of spatial prediction of a random process from a spatial dataset. Global spatial-predictor selection provides a way to choose a single spatial predictor from a number of competing predictors. Instead, we consider local spatial-predictor selection at each spatial location in the domain of interest. This results in a hybrid predictor that could be considered global, since it takes the form of a combination of local predictors; we call this the locally selected spatial predictor. We pursue this idea ...

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