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Double cross-validation reliably and unbiasedly estimates prediction errors under model uncertainty for regression models.
The difference between the raw output units reasonably estimates prediction confidence [ 11, 36].
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Box S3 of File S1, shows how to extract estimates, predictions, and samples from the fitted model.
The probability is calculated from a predicted value and the estimated prediction error, and the reliability is based on the data density.
A method for efficiently estimating prediction error bounds is presented and validated using representative parametric uncertainties.
Estimating prediction uncertainty for a single tree-based model is hindered by the complex structure of these models.
Neither mutation status nor miRNA expression improved the estimated prediction.
Late reverberation is estimated using reverberant speech and the estimated prediction coefficients.
This subsection presents an algorithm for estimating prediction error degrees using MKDA-based ordinal regression.
Figure 5 Results of estimated prediction error degrees for the meteorological element 'temperature on the ground'.
The result shows that repeated double cross-validation can be used to reliably estimate prediction errors.
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CEO of Professional Science Editing for Scientists @ prosciediting.com