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The mean accuracy achieved using an attachment (a) was 37.5% (SD = 0.3).
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For UMCS*, the accuracy of all classes were improved over the accuracy achieved by each of the individual classifiers and the average improvements reached (4.27, 3.70, and 6.41%) over the average accuracy achieved by K-means, K-medians and Kohonen respectively.
However, no differences existed in the number of subepiermal vesicular dermatitis cases seen by level of training, F 2, 68) = 1.80, MSE = 18.75, p = .17, ηp2 = .05.> Mean diagnostic accuracy achieved was 36.0 % for subepidermal vesicular dermatitides (SVD), and 39.1 % for nodular and diffuse dermatitides (NDD) (Table 3).
This is the mean and median accuracies achieved on each label when it was present in a worm.
Specifically, the mean accuracy score achieved by group 2 students on the final exam model was lower than that of group 1 students on the same exam (Mann-Whitney test, p = 0.06).
The grey dots in the background correspond to the mean classification accuracies achieved when the size of the training data is halved.
Which begs the question, how is this level of unprecedented accuracy achieved?
The best overall accuracy achieved was 56%.
Total classification accuracy achieved is 84.2%.
*100% Accuracy achieved after labelling is corrected.
A clear improvement is achieved when increasing T from 1 to 2. The highest mean accuracy is ~80.7%, achieved when T=2.
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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