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We used a fivefold cross-validation method to select the number of features; the training samples were equally split into five subsets, four subsets were used as the training dataset and the remaining one subset was used for testing.
The data was randomly split into five subsets, and five individual networks were trained each using 4/5 of the data to update the network weights and 1/5 to decide when to terminate the training (i.e. a five-fold cross-validation).
For each dataset, data points are split into five subsets of roughly equal size.
First, drugs in the gold standard set were split into five subsets of roughly equal sizes, and each subset was used in turn as a test set.
The across-country dataset was split into five subsets and, in each analysis, four subsets were used as the reference set and the other one for testing.
We performed the following 5-fold cross-validation. 1) Compound-compound pairs in the gold standard data were split into five subsets of roughly equal sizes.
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cDCs are split into two subsets: cDC1s, which specialize in CD8+ T-cell activation, and cDC2s, which specialize in CD4+ T-cell activation.
The proposed methodology is as follows: the tested flow events are split into two subsets according to the values of their rainfall variability indexes; then, a comparison is drawn between modeled and measured hydrographs separately for each subset.
To produce reliable and reproducible classification models for hERG blocking, the initial set of 180 chemicals was split into two subsets: a balanced modeling set consisting of 118 compounds and an unbalanced validation set comprised of 62 compounds.
In the second method, preambles are again split into two subsets.
For the latter, the dataset was split into seven subsets and then SRD values were calculated for each subset.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com