Cannot see the random forest for the decision trees: selecting predictive models for restoration ecology

Improving predictions of restoration outcomes is increasingly important to resource managers for accountability and adaptive management, yet there is limited guidance for selecting a predictive model from the multitude available. The goal of this article was to identify an optimal predictive framewo...

Full description

Saved in:

Bibliographic Details
Published in:	Restoration ecology Vol. 27; no. 5; pp. 1053 - 1063
Main Authors:	Barnard, David M., Germino, Matthew J., Pilliod, David S., Arkle, Robert S., Applestein, Cara, Davidson, Bill E., Fisk, Matthew R.
Format:	Journal Article
Language:	English
Published:	Malden, USA Wiley Periodicals, Inc 01.09.2019 Blackwell Publishing Ltd
Subjects:	Accuracy Adaptive management Artemisia artificial intelligence basins Data data collection decision support systems Decision trees ecological prediction ecological restoration Ecology ensemble modeling Geographic information systems Geographical information systems Learning algorithms Machine learning model comparison Modelling postfire restoration prediction Prediction models predictive framework Resource management Restoration United States United States
ISSN:	1061-2971, 1526-100X
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!