Applied Network Science

Table 4 Logistic Regression Results: One-Versus-All Classification. ROC-AUC scores for one-versus-all logistic regression experiments for all categories. Feature sets consist of the number of revisions (n_rev), control variables (CV), network metrics (NM), label frequencies (LF), and label transition probabilities (LT). Utilizing all feature sets performs best for all experiments

From: Relating Wikipedia article quality to edit behavior and link structure

Features	ROC-AUC
	LQ vs All	HQ vs All	CI vs All	EW vs All
n_rev	0.675	0.605	0.778	0.950
CV	0.720	0.702	0.789	0.970
NM	0.745	0.722	0.809	0.915
LF	0.617	0.729	0.789	0.885
LT	0.691	0.762	0.793	0.898
CV + NM	0.775	0.767	0.821	0.970
CV + LF	0.737	0.754	0.814	0.980
CV + LT	0.782	0.792	0.811	0.982
NM + LF	0.759	0.793	0.858	0.951
NM + LT	0.788	0.806	0.861	0.956
LF + LT	0.709	0.809	0.852	0.948
NM + LF + LT	0.796	0.843	0.887	0.970
CV + NM + LF	0.788	0.809	0.861	0.980
CV + NM + LT	0.814	0.825	0.866	0.981
CV + NM + LF + LT	0.823	0.853	0.888	0.986

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