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High Order Whitney Forms on Simplices and the Question of Potentials Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Logistic Regression for Prospectivity Modeling

Authors : Samuel Kost, Oliver Rheinbach, Helmut Schaeben

Published in: Numerical Mathematics and Advanced Applications ENUMATH 2019

Publisher: Springer International Publishing

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Abstract

Regression models are often employed in prospectivity modeling for the targeting of resources. Logistic regression has a well understood statistical foundation and uses an explicit model from which knowledge can be gained about the underlying phenomenon. In this paper, a model selection procedure based on logistic regression enhanced with nonlinearities is proposed. The method is designed to help the researcher in the model building process and can also be used as preprocessing step for other machine learning algorithms such as neural networks.

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Metadata
Title
Logistic Regression for Prospectivity Modeling
Authors
Samuel Kost
Oliver Rheinbach
Helmut Schaeben
Copyright Year
2021
Book
Numerical Mathematics and Advanced Applications ENUMATH 2019

Print ISBN: 978-3-030-55873-4

Electronic ISBN: 978-3-030-55874-1

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-55874-1_81

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