Logistic Regression for Prospectivity Modeling

摘要

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.