Quantitative evaluation of biophysical models of the diffusion with in vivo data by assessment of the generalization error
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چکیده
the generalization error cannot be computed exactly. Leave-one-out cross validation provides an estimate of with low bias but high variance. Kfold cross validation provides an estimate with lower variance but higher bias. Instead, Efron et al introduced the 632 bootstrap approach which counterbalances the positive bias of the fitting error by the negative bias of the leave-one out cross-validation bootstrap estimate, by computing:
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