Modelling species presence‐only data with random forests

Predictive performance of presence‐only species distribution models: a benchmark study with reproducible code

(2021) Roozbeh Valavi et al.   ECOLOGICAL MONOGRAPHS

Standards for distribution models in biodiversity assessments

(2019) Miguel B. Araújo et al.   Science Advances

Classification and regression with random forests as a standard method for presence-only data SDMs: A future conservation example using China tree species

(2019) Lei Zhang et al.   Ecological Informatics

How Complex Is Your Classification Problem?

(2019) Ana C. Lorena et al.   ACM COMPUTING SURVEYS

Application of Machine Learning to Model Wetland Inundation Patterns Across a Large Semiarid Floodplain

(2019) Sara Shaeri Karimi et al.   WATER RESOURCES RESEARCH

ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

(2017) Marvin N. Wright et al.   Journal of Statistical Software

Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance

(2016) Elizabeth A. Freeman et al.   CANADIAN JOURNAL OF FOREST RESEARCH

A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area

(2016) Farzin Shabani et al.   Ecology and Evolution

virtualspecies, an R package to generate virtual species distributions

(2015) Boris Leroy et al.   ECOGRAPHY

Is my species distribution model fit for purpose? Matching data and models to applications

(2015) Gurutzeta Guillera-Arroita et al.   GLOBAL ECOLOGY AND BIOGEOGRAPHY

Point process models for presence-only analysis

(2015) Ian W. Renner et al.   Methods in Ecology and Evolution

What do we gain from simplicity versus complexity in species distribution models?

(2014) Cory Merow et al.   ECOGRAPHY

Bias correction in species distribution models: pooling survey and collection data for multiple species

(2014) William Fithian et al.   Methods in Ecology and Evolution

Evaluating effectiveness of down-sampling for stratified designs and unbalanced prevalence in Random Forest models of tree species distributions in Nevada

(2012) Elizabeth A. Freeman et al.   ECOLOGICAL MODELLING

Species distribution modelling for conservation planning in Victoria, Australia

(2012) Canran Liu et al.   ECOLOGICAL MODELLING

Selecting pseudo-absences for species distribution models: how, where and how many?

(2012) Morgane Barbet-Massin et al.   Methods in Ecology and Evolution

Predicting disease risks from highly imbalanced data using random forest

(2011) Mohammed Khalilia et al.   BMC Medical Informatics and Decision Making

Hellinger distance decision trees are robust and skew-insensitive

(2011) David A. Cieslak et al.   DATA MINING AND KNOWLEDGE DISCOVERY

The effect of a gradual response to the environment on species distribution modeling performance

(2011) Christine N. Meynard et al.   ECOGRAPHY

Probability Machines

(2011) J. D. Malley et al.   METHODS OF INFORMATION IN MEDICINE

Poisson point process models solve the “pseudo-absence problem” for presence-only data in ecology

(2010) David I. Warton et al.   Annals of Applied Statistics

Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data

(2009) Steven J. Phillips et al.   ECOLOGICAL APPLICATIONS

Learning from Imbalanced Data

(2009) Haibo He et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Gradient modeling of conifer species using random forests

(2009) Jeffrey S. Evans et al.   LANDSCAPE ECOLOGY

Machine Learning Methods Without Tears: A Primer for Ecologists

(2008) Julian D. Olden et al.   QUARTERLY REVIEW OF BIOLOGY