Elizabeth A. Freeman

Nature and Landscape Conservation · 2006–2016

A researcher in the Science corpus: 4 papers; trusts 33 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Nature and Landscape Conservation — 26 citations into it; #755 of 6,941 by trust here, #1,940 by citations
• Ecological Modeling — 25 citations into it; #286 of 2,530 by trust here, #765 by citations
• Ecology — 25 citations into it; #3,212 of 10,717 by trust here, #5,109 by citations
• Statistics and Probability — 16 citations into it
• Artificial Intelligence — 12 citations into it; #10,716 of 35,345 by trust here, #25,809 by citations
• Environmental Engineering — 10 citations into it; #4,183 of 5,479 by trust here, #2,092 by citations
• Statistical Learning Theory — 8 citations into it

Whom Elizabeth A. Freeman trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Jerome H. Friedman
2. Antoine Guisan
3. M. Greiner
4. Gordon K. Smyth
5. Gretchen G. Moisen
6. Trevor J. Hastie
7. S. Manel
8. Greg Ridgeway
9. Elizabeth R. DeLong
10. James A. Hanley

Who trusts Elizabeth A. Freeman overall

1. Veronika Braunisch
2. Canran Liu
3. W. Bean
4. Blas M. Benito
5. Yoan Fourcade
6. K. Howell
7. Mónica López-Ratón
8. Yingxiang Huang
9. F. Ben Rais Lasram
10. A. Bari

Papers from Elizabeth A. Freeman's perspective

Ordered by how strongly the researchers Elizabeth A. Freeman trusts endorse each paper. Same corpus, a different order for every researcher.

1. Greedy function approximation: A gradient boosting machine.
2. Measuring the accuracy of diagnostic systems.
3. The meaning and use of the area under a receiver operating characteristic (ROC) curve.
4. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling
5. Evaluating the predictive performance of habitat models developed using logistic regression
6. Predicting species distribution: offering more than simple habitat models. (Ecology Letters)
7. Bagging Predictors
8. Experiments with a New Boosting Algorithm
9. Stochastic gradient boosting
10. A Coefficient of Agreement for Nominal Scales

Their papers

• Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance (2016) — 203 citations
• A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and Kappa (2008) — 616 citations
• PresenceAbsence: An R Package for Presence Absence Analysis (2008) — 514 citations
• Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods (2006) — 299 citations