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Distribution of prediction combinations.
An uncertainty analysis of the model predictions assessed the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty.
In theory, the distribution of prediction errors (varPsi) may depend on input x and therefore the length of the uncertainty interval with a pre-specified coverage probability may vary from one example to another.
The representation of the geological structure also allows a more precise definition of the spatial distribution of prediction uncertainty, here quantified with a metric based on Shannon information entropy.
In particular, this work focuses on the correlation between the GPM prediction variance and the distribution of prediction errors over multiple experimental designs, as a function of location in the input space.
Also, this article concludes that the distribution of prediction errors approaches a normal distribution with a variance equal to the GPM prediction variance, even in the presence of significant bias in the GPM predictions.
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The distributions of predictions and errors for the training pool accurately predicted the distribution of predictions and errors for large external validation sets, even when the number of positive and negative examples in the training pool were not balanced.
The distribution of predictions indicates that there is much more fear of a negative surprise than there is hope for a positive one.
(A) Distribution of predictions (blue) and errors (red) for the external validation set.
(C) Distribution of predictions (blue) and errors (red) for the external validation set.
(A and C) Distribution of predictions (blue) and errors (red) for the external validation set.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com