<p>Five-fold cross-validation of the linear (blue bars) and the nonlinear (yellow bars) models on the simulation data generated with an ODE-based system. For example, 999 out of the total 1000 predictions made by the nonlinear model have both the Spearman and the Pearson correlations with the testing data higher than 0.7.</p
that leave-one-out cross-validation is not subject to the “no-free-lunch ” criticism. Despite this o...
<p>Each plot follows the performance of a regression model as complexity increases. For lasso (top p...
<p><sup>1</sup> MSEP = mean square error of prediction and CCC = concordance correlation coefficient...
<p>Blue bars and yellow bars indicate the numbers of validations which have both Spearman and Pearso...
Frequently, the main objective of statistically designed simulation experiments is to estimate and v...
<p>In this toy model, we try predict a variable from an uncorrelated predictor. The predictive power...
<p>Empirical significance was obtained from the fraction of permutations that showed a correlation h...
<p>The upper panel illustrates the combination of the inner cross-validation loop, which is used to ...
<p>Results for feature selection, model selection and validation, using the two selection criteria a...
Linear regression metamodels have been widely used to explain the behavior of computer simulation mo...
Cross-validation is frequently used for model selection in a variety of applications. However, it is...
Cross-validation is frequently used for model selection in a variety of applications. However, it is...
Statistics of cross validated models trained with tiE energy terms versus experimental dTm values, w...
A) Pearson linear correlation, B) Mean absolute error, C) Spearman rank correlation, D-E) Accuracy a...
Performance metrics for spatial and linear models from 10-fold cross-validation simulations.</p
that leave-one-out cross-validation is not subject to the “no-free-lunch ” criticism. Despite this o...
<p>Each plot follows the performance of a regression model as complexity increases. For lasso (top p...
<p><sup>1</sup> MSEP = mean square error of prediction and CCC = concordance correlation coefficient...
<p>Blue bars and yellow bars indicate the numbers of validations which have both Spearman and Pearso...
Frequently, the main objective of statistically designed simulation experiments is to estimate and v...
<p>In this toy model, we try predict a variable from an uncorrelated predictor. The predictive power...
<p>Empirical significance was obtained from the fraction of permutations that showed a correlation h...
<p>The upper panel illustrates the combination of the inner cross-validation loop, which is used to ...
<p>Results for feature selection, model selection and validation, using the two selection criteria a...
Linear regression metamodels have been widely used to explain the behavior of computer simulation mo...
Cross-validation is frequently used for model selection in a variety of applications. However, it is...
Cross-validation is frequently used for model selection in a variety of applications. However, it is...
Statistics of cross validated models trained with tiE energy terms versus experimental dTm values, w...
A) Pearson linear correlation, B) Mean absolute error, C) Spearman rank correlation, D-E) Accuracy a...
Performance metrics for spatial and linear models from 10-fold cross-validation simulations.</p
that leave-one-out cross-validation is not subject to the “no-free-lunch ” criticism. Despite this o...
<p>Each plot follows the performance of a regression model as complexity increases. For lasso (top p...
<p><sup>1</sup> MSEP = mean square error of prediction and CCC = concordance correlation coefficient...