Titlebook: Machine Learning for Ecology and Sustainable Natural Resource Management; Grant Humphries,Dawn R. Magness,Falk Huettmann Book 2018 Springe

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Machine Learning in Wildlife Biology: Algorithms, Data Issues and Availability, Workflows, Citizen Sresting uses of these sophisticated algorithms which are driving inference and understanding in natural resource management. The concept behind machine learning is to provide data to a computer and allow the machine to ‘learn’ the patterns in those data. These learned relationships are applied and a

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From Data Mining with Machine Learning to Inference in Diverse and Highly Complex Data: Some Shared over several hundred years (without computers), and it is usually centered around frequency mindsets and central theorems, summarized by Zar (.). Nowadays, statistics are easily done with a computer and the internet, which brings forward new approaches to analysis and inference. Traditional (freque

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Ensembles of Ensembles: Combining the Predictions from Multiple Machine Learning Methodsof their strengths and weaknesses in applied contexts. Tree-based methods such as Random Forests (RF) and Boosted Regression Trees (BRT) are powerful ML approaches that make no assumptions about the functional forms of the relationship with predictors, are flexible in handling missing data, and can

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Machine Learning for Macroscale Ecological Niche Modeling - a Multi-Model, Multi-Response Ensemble Tlethora of techniques based on ensemble methods. In this chapter, I explore techniques relevant to macroscale ecological niche modelling in a regression context. I evaluate the challenges while predicting suitable habitats under future climates, and address issues related to high dimensional data li

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