A validation confidence interval satisfying (11) allows a rejection of the model if a noisy validation measurement with error SD is outside the interval.
Three 100 × 100 m raster-based built-up maps for 1990, 2000, and 2010 are developed to define one calibration interval (1990 2000) and one validation interval (2000 − 2010).
Here ∆t refers to the CUG validation interval.
Times are given in hours, for a single cross-validation interval In the current study, we have presented a detailed description of a new methodology to detect gene-gene interactions in genetic association studies.
These results are the average measures of model fit in the testing (not training) sets across all cross validation intervals.
We then defined the validation to be successful if the values of several statistics in the closed part of the database, most importantly the positive predictive value, are above the lower endpoints of approximate one-sided 95% prediction (99% for "excellent" validation) intervals for the anticipated value in the validation part of the database.
As described above, for each cross-validation interval, a best model is chosen based on highest accuracy of all models evaluated for that interval - resulting in a total of 10 models (one best model for each interval).
The final models are listed in Additional file 4, with the logistic regression equation (with parameter estimates and included variables listed) for each cross-validation interval.
Remarkably, this discrimination was achieved with two metabolites that consistently were included in each cross-validation interval (5/5 cross-validation consistency): 15-65.533 and 8-93.65 the identities of which are currently unknown.
In each cross-validation interval, the variables included were recorded, as the final model was selected based on cross-validation consistency (picking the variable[s] that were selected in the most cross-validation models).
This provides a summary of the number of cross-validation intervals in which a particular model was found.
