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Note the high performance of the trivial predictor (70%-91 70%-91
We furthermore note the impressive performance of the trivial predictor at large spatial scales.
This predictor will be referred to as "the trivial predictor" and it measures the amount of stationarity in the mobility sequence.
We find that the trivial predictor is successful in (88.3 pm3.8 %) of the cases, while the upper bound is (95.5 pm1.8%%), i.e. almost all of the predictability reflects the fact that people do not change location.
The lower limit is found to be (Pi^{mathrm{min}} = 39.8 pm 5.9%%), which is at least 30% lower than any of the lower limits found by the trivial predictor for next bin sequences.
In this study however, advanced age was found to be a trivial predictor of mortality.
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What was the predictor?
This tiredness during daily activities is not a trivial symptom but a predictor of disability and mortality in older individuals [ 13, 14].
The extraction of information on interactions between predictor variables based on random forests is not trivial.
In contrast, the Oncotype DX and IR module classifiers are each defined by a single centroid and therefore constitute (modulo a trivial gene-wise recentering and rescaling) bona-fide single-sample predictors.
Thus, this example shows the properties of useful predictor for genetic redundancy, accurately delineating redundancy within a gene family in a non-trivial way; that is, the predictor does not simply label the most closely related genes as redundant.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com