The ROC curves and AUC values of the diagnostic model. by Zhe Wu (582498)

ROC curve and AUC value of all models. by Tiago de Oliveira Barreto (20485207)

“…As for Specificity (82.94%) and ROC-AUC (82.13%), the Multilayer Perceptron with SGD optimizer obtained the highest scores. …”

L* Value Increases with Increasing Zinc Oxide Percentage. by Alexandra M. Maldonado López (22127340)

TITAN thresholds and percentile estimates for benthic macroinvertebrate and diatom communities deemed to be sensitive decreasers or tolerant increasers. The thresholds represent the largest fsum <i>z</i> value in the main data analysis run (i.e., the median), whereas the 5^th and 95^th percentile change points are determined from 500 bootstrap replicate runs.... by Brent J. Bellinger (21156150)

“…<p>TITAN thresholds and percentile estimates for benthic macroinvertebrate and diatom communities deemed to be sensitive decreasers or tolerant increasers. The thresholds represent the largest fsum <i>z</i> value in the main data analysis run (i.e., the median), whereas the 5^th and 95^th percentile change points are determined from 500 bootstrap replicate runs. …”

Changes in objective value for increasing hospital capacity. by Tingting Zhang (264633)

“…<p>Changes in objective value for increasing hospital capacity.</p>…”

Normalized sensitivity indices of with increasing the parameter values. by Mian Imad Shah (22656439)

“…<p>Normalized sensitivity indices of with increasing the parameter values.</p>…”

Area under the curve (AUC) for external validation performance. by Junichi Kushioka (12236447)

Table of objective values of feasible solutions by increasing value from top to bottom. by Hoang Minh Dang (22208489)

“…<p>Table of objective values of feasible solutions by increasing value from top to bottom.…”

Missing value percentage for variables. by Xin Liu (43569)

Daily number of adverse outcomes (bars) and area under the precision-recall curve (AUC-PRC) per day of the fullPIERS model. by Guiyou Yang (10259597)

Subjects:

Daily number of adverse outcomes (bars) and area under the precision-recall curve (AUC-PRC) per day of the PIERS-ML model. by Guiyou Yang (10259597)

Subjects:

AUC statistics as calculated from simulated time series. Each statistical metric was calculated within sliding windows, throughout the pre-critical interval. We considered five-, fifteen-, and thirty-day sliding windows. Given that the temperature of the system increased to 12°C on day sixty, we also considered three pre-critical intervals: Days 1 to 60, Days 20 to 60, and Days 30 to 60. To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval, and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger tre by Madeline Jarvis-Cross (22394247)

“…To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval, and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger tre</p>…”

TITAN increaser diatom heatmap. by Brent J. Bellinger (21156150)

Motif enrichment values at promoters of genes with increased expression at TL. by Nawrah Khader (9128150)

“…<p>Motif enrichment values at promoters of genes with increased expression at TL.…”

AUC of various models to predict imeglimin efficacy for liver function evaluated by ROC curves. by Naruyoshi Gyoutoku (22537831)

Column charts of WiAUC in therapy and control group at baseline and follow-up. by Felix L. Herr (21544362)

Feature engineering. by Youli Jiang (15249070)

Subjects:

AUC ROC curve. by Dieuwke Luijten (15282736)

Absolute values of our five metrics for increasing levels of fibrosis, fully fibrotic simulation. by Åshild Telle (22794721)

“…<p>Absolute values of A-loop area (stroke work), booster function, reservoir function, conduit function, and upstroke pressure difference for Patients 1–3 (P₁–P₃). …”

AUC statistics comparing statistical trends in control and test populations. by Madeline Jarvis-Cross (22394247)

“…<p>To evaluate statistical trends, we calculated Kendall’s rank correlation coefficient during the pre-critical interval (here, days one to sixty), and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger trends. …”