scholarly journals A Decision Support Tool for Predicting Patients at Risk of Readmission: A Comparison of Classification Trees, Logistic Regression, Generalized Additive Models, and Multivariate Adaptive Regression Splines

2014 ◽  
Vol 45 (5) ◽  
pp. 849-880 ◽  
Author(s):  
Eren Demir
Keyword(s):  
Logistic Regression ◽  
Generalized Additive Models ◽  
Decision Support Tool ◽  
Additive Models ◽  
Multivariate Adaptive Regression Splines ◽  
Regression Splines ◽  
Support Tool ◽  
Patients At Risk ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

scholarly journals Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds

2016 ◽  
Vol 20 (7) ◽  
pp. 2611-2628 ◽  
Author(s):  
Julie E. Shortridge ◽  
Seth D. Guikema ◽  
Benjamin F. Zaitchik
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Multivariate Adaptive Regression Splines ◽  
Regression Splines ◽  
Climate Conditions ◽  
Machine Learning Methods ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

Abstract. In the past decade, machine learning methods for empirical rainfall–runoff modeling have seen extensive development and been proposed as a useful complement to physical hydrologic models, particularly in basins where data to support process-based models are limited. However, the majority of research has focused on a small number of methods, such as artificial neural networks, despite the development of multiple other approaches for non-parametric regression in recent years. Furthermore, this work has often evaluated model performance based on predictive accuracy alone, while not considering broader objectives, such as model interpretability and uncertainty, that are important if such methods are to be used for planning and management decisions. In this paper, we use multiple regression and machine learning approaches (including generalized additive models, multivariate adaptive regression splines, artificial neural networks, random forests, and M5 cubist models) to simulate monthly streamflow in five highly seasonal rivers in the highlands of Ethiopia and compare their performance in terms of predictive accuracy, error structure and bias, model interpretability, and uncertainty when faced with extreme climate conditions. While the relative predictive performance of models differed across basins, data-driven approaches were able to achieve reduced errors when compared to physical models developed for the region. Methods such as random forests and generalized additive models may have advantages in terms of visualization and interpretation of model structure, which can be useful in providing insights into physical watershed function. However, the uncertainty associated with model predictions under extreme climate conditions should be carefully evaluated, since certain models (especially generalized additive models and multivariate adaptive regression splines) become highly variable when faced with high temperatures.

scholarly journals Comparison of Logistic Regression Model and MARS Using Multicollinearity Data Simulation

2020 ◽  
Vol 4 (1) ◽  
pp. 39
Author(s):  
Ananto Wibowo ◽  
M. Rismawan Ridha
Keyword(s):  
Logistic Regression ◽  
Principal Component ◽  
Multivariate Adaptive Regression Splines ◽  
Predictor Variables ◽  
Regression Splines ◽  
Data Simulation ◽  
Simulation Data ◽  
Adaptive Regression ◽  
Categorical Response ◽  
Adaptive Regression Splines

There are several statistical methods used to model the effect of predictor variables on categorical response variables, namely logistic regression and Multivariate Adaptive Regression Splines (MARS). However, neither MARS nor logistic regression allows multicollinearity on any predictor variables. This study applies the use of both methods to the simulation data with principal component analysis as an improvement in multicollinearity to find out which regression has better performance. The result of the analysis shows that MARS is very powerful in modeling research simulation data. Besides, based on the criteria of the number of significant major components, accuracy, sensitivity, and specificity values, MARS has more appropriate performance than logistic regression.

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