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The significant characteristics and selected characteristics identified by the variable selection method differed somewhat according to each model.
Using a reduced set of important variables (i.e. reducing the dimensionality of input space by the variable selection procedure) results in support vector machines with better performance.
Family history of ovarian cancer was dropped by the variable selection analysis.
More stable and robust models were achieved by the variable selection method, known as Martens' uncertainty test [ 43].
Table 4 shows the optimal subset of regressors obtained step-by-step by the variable selection procedure.
Reducing the complexity of a model and obtaining an easier model interpretation and a possible improvement in prediction can be achieved by the variable selection.
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Cross model validation is designed to validate the optimisation by including the variable selection in an extra layer of cross-validation.
By invoking the variable selection procedure embedded in the multivariate regression analysis, the significance of variables to the responses was assessed.
By means of the variable selection and modeling method based on the prediction (VSMP), it was found that the higher the concentration of [C2mim]Cl is, the stronger the mixture hormetic effect is and the higher the concentration of [C12mim]Cl is, the weaker the hormetic effect is.
The optimal subset of descriptors is chosen by any one of the variable selection methods which are most likely to describe all the physicochemical properties of the congeneric series required for their biological activity.
We protected against overfitting of the GAM by using cross-validation in the variable selection process (Guisan and Zimmermann 2000; Hawkins 2004; Packalén et al. 2012); however, the smoothing splines of GAM's may perform in an unpredictable manner when applied to population-level data which includes ranges of values not within the observation data (Guisan et al. 2002; Venables and Dichmont 2004).
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