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The adjusted R2 for this equation was 54.9% which is a reliable value for evaluating prediction accuracy.The average error between the predicted increment and the actual increment was 0.149 mm for our method and 5.87 mm for the method by Mito et al. There are two items that contributed to easier and better prediction accuracy in our equation: (1) higher R2 and (2) fewer independent variables.
Shepperd and MacDonell "Evaluating prediction systems in software project estimation".
Developing and evaluating prediction models in rehabilitation populations.
When evaluating prediction models, test set data are typically used to assess model performance with unknowns.
For presence/available models we propose a form of k-fold cross validation for evaluating prediction success.
For example, in (Shepperd and MacDonell 2012) the authors proposed a framework for evaluating prediction systems to reduce the inconsistency amongst validation study results and provide a more formal foundation to interpret results on continuous prediction systems.
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It's largely about evaluating predictions in a variety of fields, from finance to weather to epidemiology.
A new way of evaluating predictions has to be developed to renew the usual binary way to discriminate companies.
Evaluating predictions at the "pixel" scale may give a false picture of fine-scale accuracy.
In evaluating predictions, we focused on the association between predictions and TSRs or gene starts.
Since the performance of different algorithms depends on target difficulty, taking this characterization into account becomes essential for evaluating predictions.
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