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The ranking across classification methods is similar to the previous one: SOC HE 2010_GH explains the highest fraction of inter-individual wage differences.
The ROC area under the curve (AUC) values for randomly selected test locations (25% of data withheld from models) were compared to evaluate model performance across classification thresholds.
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This allows for general comparisons of magnitudes of strength both within and across classifications.
In contrast, there were many instances in which forest classes that did not correspond well across classifications.
The variation in the proportion of unexplained stillbirths across classifications is at least partly explained by the classification system.
aIPS, aIPS, t-aIPS and motor cortex) that, although able to predict upcoming grasp vs reach movements with both the hand and the tool, did not generalize across the effector (i.e., no across-effector classification).
Likewise, in motor cortex we show decoding for upcoming hand- and tool-related actions but, importantly, find no resulting across-effector classification.
Likewise, there is nothing in the univariate response properties of areas aIPS and PMd to suggest that only the latter area will show accurate across-effector classification.
If successful, this cross-classification would suggest that the object-directed action plans being decoded are to some extent independent of the acting effector (at least to the extent that accurate across-effector classification can be achieved).
DOI: http://dx.doi.org/10.7554/eLife.00425.008 10.7554/eLiFigure25.009 Figure 4 figure supplement 1. Time-resolved and plan-epoch decoding accuracies for across-effector classification, separated according to the direction of classifier training and testing.
MVPA performance significantly improved from using a large number of short runs, compared to several long runs, in across-run classifications with identical amounts of data.
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