ISPRS International Journal of Geo-Information | |
Targeting: Logistic Regression, Special Cases and Extensions | |
关键词: prospectivity modeling; potential modeling; conditional independence; naive Bayes model; Bayes factors; weights of evidence; artificial neural nets; imbalanced datasets; balancing; | |
DOI : 10.3390/ijgi3041387 | |
来源: mdpi | |
【 摘 要 】
Logistic regression is a classical linear model for logit-transformed conditional probabilities of a binary target variable. It recovers the true conditional probabilities if the joint distribution of predictors and the target is of log-linear form. Weights-of-evidence is an ordinary logistic regression with parameters equal to the differences of the weights of evidence if all predictor variables are discrete and conditionally independent given the target variable. The hypothesis of conditional independence can be tested in terms of log-linear models. If the assumption of conditional independence is violated, the application of weights-of-evidence does not only corrupt the predicted conditional probabilities, but also their rank transform. Logistic regression models, including the interaction terms, can account for the lack of conditional independence, appropriate interaction terms compensate exactly for violations of conditional independence. Multilayer artificial neural nets may be seen as nested regression-like models, with some sigmoidal activation function. Most often, the logistic function is used as the activation function. If the net topology,
【 授权许可】
CC BY
© 2014 by the authors; licensee MDPI, Basel, Switzerland
【 预 览 】
Files | Size | Format | View |
---|---|---|---|
RO202003190018776ZK.pdf | 382KB | download |