We may see a transition from testing for technical completion of a workflow towards the finding of regressions in the performance of the tool.
Although, linear regression (LR) technique showed satisfactory results, neural network technique outscored linear regression in the prediction performance, and generalization ability.
Process expertise was, however, associated with a small, but statistically significant, improvement in performance (regression coefficient 2.7 [0.1, 5.4], p <.05).The difference in performance associated with process expertise had a small to medium effect size (Cohen's d =.40).
Correlations were tested using the group-level multiple regression in SPM, with each performance measure being regressed voxel-by-voxel onto corresponding contrast images (e.g. ambiguous-unambiguous). Results were subject to the same statistical thresholds as one-sample t-tests.
We observe that lasso and dirty regularization-based regression methods yield significant improvements in performance, both yielding 78.4% prediction accuracy.
"For example, we wouldn't be able to track down a 1percentt performance regression in a simulator," Reversat says in today's formal announcement.
As in the regression experiment, performance differences are especially noticeable over synthetic data.
Two variables used in the regression models, performance status and pretreatment stage, were known for 72%and61%1% of the patients, respectively.
Single and multi-level regression analyses were used to investigate changes in performance of the finishers.
Single and multi-level regression analyses were used to investigate changes in performance.
