A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility

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Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review (2020, methodology): “Each internal node contains a test that best splits the space of data to be classified [17].”
A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment (2018, methodology): “Logistic model tree The LMT method combines decision tree learning methods and logistic regression (Tien Bui et al. 2016b; Chen et al. 2017i).”
New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed (2019, background): “As yet, there is no universal approach for preparing landslide susceptibility maps, thus landslide researchers are experimenting with new methods and techniques [6].”
Text Classification Algorithms: A Survey (2019, background): “One of the simplest classification algorithms is logistic regression (LR) which has been addressed in most data mining domains [15–18].”