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The number of source parameters that we used to assess prediction uncertainty was more than 12.
As a general rule, external validation tests are considered the true standard to assess prediction in QSAR modeling.
This simultaneous calculation is a big advantage compared to other approaches, which assess prediction reliability and usually need additional iterations.
Of interest, a recent pilot study examined the use of actigraphy to assess prediction for different bipolar clinical states including mania, depression, and mixed (Scott et al. 2017).
Our rationale here is grounded on the counter question: What else could be more relevant to assess prediction rather than its correctness?
The root mean squared error, mean error and mean absolute error calculated from leave-one-out cross-validation were used to assess prediction accuracy.
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The experimental task comprised several learning conditions which were designed to assess predictions derived from the inhibition-deficit hypothesis.
They assess predictions using historical entries from before 2000, identifying genes and keywords from entries using text mining.
In addition, we re-analyzed data from four groups of reef fish species (the wrasse genus Halichoeres [ 36], and the parrotfish genus Sparisoma [ 38]) to specifically assess predictions from the center of origin and center of accumulation hypotheses.
We explored the relationship between exponential growth of RSV epidemics and the seasonal epidemic characteristics of total epidemic size, days to peak, and epidemic length to assess predictions made early in the epidemic season.
The 2-week timepoint chosen for analysis is shorter than the typical time between tumor scans (bimonthly); however, longer simulated treatment durations did not affect rank order of treatment effects, the metric used to experimentally assess predictions.
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