Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size

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A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility (2017, background): “…(Lee et al., 2007; Park et al., 2013; Tien Bui et al., 2016c; Yilmaz, 2010), fuzzy k-nearest neighbor (Tien Bui et al., 2016a), Naïve Bayes (Tsangaratos and Ilia, 2016), support vector machines (Chen et al., 2016b; Colkesen et al., 2016; Xu et al., 2012), leastsquared support vector…”
Spatial Prediction of Rainfall-Induced Landslides Using Aggregating One-Dependence Estimators Classifier (2018, methodology): “The NB has been applied effectively for landslide spatial prediction (Tsangaratos and Ilia 2016; Tien Bui et al. 2012a).”
Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree (2019, methodology): “…2015; Polykretis et al. 2015), support vector machines (Hong et al. 2017a; Chen et al. 2018c; Zhou et al. 2018), naive Bayes (Tien Bui et al. 2012a; Tsangaratos and Ilia 2016; Pham et al. 2016a), and boosted regression trees (Youssef et al. 2016) have been widely used for landslide susceptibility…”
Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms (2018, background): “The result reveals that the NB model had great predication accuracy [79].”