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τ = 12hr, accuracy = 84%, most predictive features: 1).
#posts; τ = 24hr, accuracy = 92%, most predictive features: 1).
There are several methods to select the most predictive features in a classification task [42].
Table 4 Most predictive features in EU and SA according to the Linear SVM with L1 penalty In EU 1.
In addition, to determine the most predictive features, we rank the features using Chi-square feature selection.
More quantitatively, applying Algorithm EW with the four meme dynamics features produces the following results (ten-fold cross-validation): τ = 12hr, accuracy = 84%, most predictive features: 1).
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The sole focus of most predictive feature selection techniques is on defeating the curse of dimensionality.
The most predictive feature is gene expression in She4 deletion mutants.
Unlike conventional trees, where each node is split using the overall most predictive feature, each node in random forest trees is split using the most predictive feature from a subset of features randomly sampled at that node.
This again is consistent with the results shown in our single feature analysis, where the phyletic retention measure was by far the most predictive feature.
The set of microbiome features used in the cross-donor analysis is taken from the most predictive feature subsets from the previous results, based on the combined Donor A and B data (Fig. 6).
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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