Concerning the comparison of the accuracies of the two upscaling approaches: based on the cross-validation results (Table 2 and 3), the UM2 is generally better than UM1 in terms of smaller mean and median errors and higher success rates of passing the chi-square tests of uncertain information.
We give the median error, the mean error, and the root mean squared error for the X and Y dimensions as well as the Euclidean prediction errors, the 90'th percentile error, (the max error after dropping 10'th percentile of spots with the worst error,) and the root mean square error of the 90'th percentile of spots with the least error.
Further, we find that AncesTree achieves a median error on F and U that is 0.01 and 0.03 lower than the median error of the other methods.
In Table 1, we show the mean and median error (absolute difference between known and estimated allele frequency), the correlation coefficient between known and estimated allele frequency, and the standard deviation (SD) between the four replicates.
Our results suggest that the support vector regression model can outperform the life usage model on the evaluation measures of sample standard deviation, median error, median absolute error, and percentage error.
Values of median relative error and median relative HPD range are plotted against the number of loci, where the number of taxa is fixed to 15.
Median performance error and median absolute performance error were -2.9 and 7.0% in the 3.5% after 3.5 min group, -3.4 and 11.4% in the 5% after 5 min group, and -16.2 and 16.2% in the 6% after 4.5 min groups, respectively.
The median prediction error and the median of individual median absolute prediction error were 2.44% and 21.6%, respectively.
Figure 11 Median error between predicted and observed votes 24 Digg hours after promotion for predictions made 0, 2, 4 and 6 Digg hours after promotion for 500 stories.
