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Because the CDEN is based on the MLP, nonlinear relationships, including those involving complicated interactions between predictors, can be described by the modeling framework.
Complex nonlinear relationships can be fit, and interactions between predictors handled automatically.
Complex nonlinear relationships can be fitted, and interactions between predictors handled automatically.
We were unable to evaluate interactions between predictors because of insufficient degrees of freedom (see Table 2 for the models considered, AICC values, and Akaike weights).
Regression trees have several advantages as a modeling technique, including that various types of predictor and response variables can be analyzed simultaneously rather than in an iterative manner, missing values in data sets can be incorporated and therefore information loss minimized, and complex interactions between predictors can be quantified and modeled in a simple manner [44].
Interactions between predictors were explored.
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#12 category * p < 0.05 **p < 0.01 ***p < 0.001 Potential interactions between predictor variables and stigma types were investigated using a series of Mixed Between-within subjects Analyses of Variance (Between variable = Predictor; Within Variable = Stigma Type).
Neural network models have the ability to detect all possible interactions between predictor variables.
Incorporating interactions between predictor variables increased the predictive power of our models, particularly for Porites trematodiasis (PorTrem).
Previous models identified other interactions between predictor variables.
We examined interactions between predictor variables and age.
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