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This scheme intends to balance the two classes during each binary partition.
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Since both data sets are strongly unbalanced between two (metastatic and non-metastatic) phenotypes, we balanced the two classes for classification purposes (in training and test sets) by bootstrapping from the larger collection of non-metastatic samples using 5-fold undersampling, with each sample matched in size to that of the metastatic group.
This enables locusts, rats, cattle, and humans to choose the food type most needed at a particular time and thus, over a period of time, achieve a suitable balance between the two classes of nutrients.
At this point, we should mention that the involvement of "dummy" devices, namely legacy devices with no intelligence or self-x mechanisms, does not affect the load-balancing mechanism for the two classes of "intelligent" devices.
The two classes were balanced for length (common nouns = 7.60; proper names = 7.34; these values were statistically identical (F 1,378) = 2.186; p = 0.1401) and written frequency of use (common nouns = 38.75; proper names = 32.22; these values were also statistically identical: F 1, 378) = 0.966; p = 0.3264).
G-mean that tries to maximize the accuracy on each of the two classes while keeping them balanced is calculated as following: (4) The training set (dataset I) contained 390 active compounds and 146 934 inactive compounds, with <0.3% of the data active and ∼99.7% inactive resulting in a ratio of active to inactive of 1/377.
> -wrap-foot> We should also consider that the predictions are very well balanced, reaching the same sensitivity and specificity values for the two classes.
Since the instance counts of different labels are not balanced in this dataset, assignment of the same cost value to the two classes would cause skewing of the separating hyperplane towards the minority class as a result of this imbalance.
The balance of this equilibrium is determined by a combination of intracellular signals and extracellular ligand concentrations, with the two classes of signal integrated by individual integrin heterodimers.
Histone acetylation is mediated by a balance between histone acetyltransferases (HATs) and the three classes of histone deacetylases (HDACs): Class 1 (HDAclass8), class 2 (HDand-7,9-11), and class 3 (Sirt1-7).
Therefore, the nature, duration, and intensity of a specific immune response and its effect on the stress axis may depend on a delicate balance between these two classes of cytokines (Marshall 2004).
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