Your English writing platform
Discover LudwigSuggestions(1)
Exact(59)
Once the ensemble is trained, we evaluate its predictive power on the testing set.
Table 1 The example of predictive probability of classifier NT1/NT2 on the testing set.
Next we evaluate the performances of the built SGB model in the testing set.
Impressively, the NSAE-LCN achieved greater than 99.9% accuracy of classification on the testing set.
The rest belong to the testing set.
Again, the rest consist the testing set.
The testing set consisted of 50 additional parallel sentences.
The rest of the video's frames consist the testing set.
The results using the testing set are shown in Table 3.
In each cycle, the samples in the testing set are included into the current training set.
The testing set and associated statistical performances are reported in the last column of Table 4.
Write better and faster with AI suggestions while staying true to your unique style.
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