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. 2020 Dec 1;4(4):1197–1218. doi: 10.1162/netn_a_00160

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© 2020 Massachusetts Institute of Technology

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.

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Figure 3. — Cross-validation of graphical lasso (GL) and adaptive graphical lasso (AGL). We show performance (cross-validated deviance) across penalization for the GL (on the left) and AGL (right figure). The error bars represent the standard deviation of the deviance across patients. We found that for all but very high penalization the AGL outperformed the GL. Further, the AGL shows minimal cross-validated deviance for an optimal set of SC edges (at an SC penalization of 0.125) while the graphical lasso shows minimum deviance only for very sparse models.