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Of the 37 breeding colonies of the test data set, the model correctly classified 31 (83.78%), the same percentage as the correctly classified absences (i.e. 31 out of 37).
The model correctly classified 83.7% of 132 mps and 98.89% of 274 control sequences in training.
The DA model correctly classified 58% of the selection outcomes in mussels and 57% in oysters.
The model correctly classified 88.47% and 92.75% of active and inactive compounds respectively, in training set.
The QSAR model correctly classified 88.55% of compounds in this external prediction set, yielding a MCC of 0.77.
In cross-validation the model correctly classified 71 and 70% of observations in the model-building and validation samples, respectively.
The logistic model correctly classified 95.5% of sample squares of bear presence and 93.8% of those where bears were absent.
The overall model correctly classified 80% of the sample as accepters or nonaccepters of the vaccination for their child.
Through a combination of childhood predictors, the model correctly classified 82%2727 of 33) of the participants who eventually developed a psychosis-spectrum outcome in adulthood.
Our model correctly classified severity with a classification accuracy of 79.5% when burn severity pixels were classified as severe vs. not severe (two classes).
The empirically derived model correctly classified only 52% of recent bobcat locations, whereas the process-oriented model indicated that nearly 88% of recent bobcat observations were associated with sites that were ranked at high suitability.
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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