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Table 2 Hyper-parameters valuexploredred for Bernoulli Naïve Bayes, k-nearest neighbor, random forest, support vector machines and deep neural networks Hyper-parameters Values exParameterrameter Bernoulli Naïve Bayes Alpha 1, 0.5, 0.1 Laplace/Lidstone smoothing parameter Fit_prior True, false Class prior probabilities.
In order to assess whether the WAAC algorithm sufficiently explored parameter space, we carried out a parameter scan across all allowed values for the parameter with the feature selection found in the best model, and calculated the value of the objective function, RMSE int).
Note in Supplementary Table S1 that this result for C = 6 is unchanged for all combinations of wild type mutation rate, cell generations to cancer, and degree of fitness increase within the explored parameter values.
All studies, with the exception of one, explored parameter uncertainty.
All but one study [ 26] explored parameter uncertainty.
Extensive sensitivity analyses explored parameter uncertainty and aspects of the structural uncertainty (e.g., different cohort profiles).
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Significant variations in enrichments were observed with all explored parameters.
We explored parameters of EMT including cytoskeletal proteins, cell adhesion and migration, and examined their changes in early stages of transformation.
We investigated pregnancy outcome following transabdominal cerclage (TAC) in women with cervical insufficiency (CI) and explored parameters for predicting pregnancy outcomes following TAC.
> -wrap-foot> *To estimate the odds ratio mortality in the group, >4 h is set as 1 In the univariate and multivariate analysis, we explored parameters on admission predictive for early clinical failure in severe CAP.
Blending can also be usefully employed to interactively explore parameter variations for enhancement techniques.
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