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Table 2 shows the head of the sampled training subset data, while Table 3 shows the head of the sampled testing subset data.
Also, we denote a set of these sampled training data as T N. By reducing the number of training samples, the performance of the KDA-based ordinal regression tends to become worse.
Since different sets of randomly sampled training points should produce different model outcomes, we assessed the effect of training point variability on model performance.
We test our best selected setup, i.e. the SVM model with C=2−6 on the locally sampled training set using the combined feature set, on the independent test dataset (using the model built on the training dataset).
On average, across 100 randomly sampled training and validation sets, model 7 had a significantly (p <0.0001) greater prediction correlation coefficient (r =0.86 ± 0.01) than model 1 (r =0.83 ± 0.01; Additional file 2: Figure S1).
This means that the fitting of γ tends to strongly depend on the sampled training data.
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After the free app is installed, you can try a sample training battle with no plane required.
Figure 9 plots the results for the out-sample training.
Table 1 Data features and diversity Dataset Number of features Sample Training sample Test sample 1 15 325 213 123 2 14 287 145 98 3 12 234 89 45.
As in [5], we consider 3 years of in-sample training, in detail (3times 365) days.
As the basis of image recognition, such information can provide sample training and quick identification.
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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