• graphs
• Figure 2 shows the resultant ROC graphs while Table 1 shows the corresponding AUC scores. (nih.gov)
• ROC graphs enable to compare two or more supervised learning algorithms, they have properties that make them especially useful for domains with skewed class distribution and unequal classification error costs. (blogspot.com)
• Multiple Antigen LOOCV ROC curves.The LOOCV ROC graphs show classifiers with increasing number of human serodiagnostic antigens. (nih.gov)
• plots
• The rocsnr function plots the ROC curves by default if no output arguments are specified. (mathworks.com)
• The Gini coefficient is usually defined mathematically based on the Lorenz curve, which plots the proportion of the total income of the population (y axis) that is cumulatively earned by the bottom x% of the population (see diagram). (wikipedia.org)
• area
• The serodiagnostic antigens were ordered by decreasing single antigen area under the curve (AUC). (nih.gov)
• The largest area under these three ROC curves is 0.898, indicating that statistically the best combination is VPG as reference with the CBG results as the screening tool. (nih.gov)
• The smaller area under the curve for the case where HbA1c is the reference and VPG is the screening tool is 0.733. (nih.gov)
• The area under the ROC curve for TGF-β1 (0.693) and MCP-1 (0.653) indicated that these cytokines could serve as biomarkers for the progression of CWP. (nih.gov)
• K {\displaystyle K} denotes lifetime morbid risk, h L 2 {\displaystyle h_{L}^{2}} denotes heritability of liability, A U C {\displaystyle AUC} denotes area under the ROC curve. (wikipedia.org)
• The index is represented graphically as the height above the chance line, and it is also equivalent to the Area under the Curve subtended by a single operating point. (wikipedia.org)
• analysis
• The discriminative capacity of the 2 assays was comparable, even if total-anti-HBc performed better than IgM-anti-HBc (p = 0.212 and p = 0.062 for ROC analysis a and b respectively). (nih.gov)
• ROC analysis provides tools to select possibly optimal models and to discard suboptimal ones independently from (and prior to specifying) the cost context or the class distribution. (wikipedia.org)
• ROC analysis is related in a direct and natural way to cost/benefit analysis of diagnostic decision making. (wikipedia.org)
• ROC analysis since then has been used in medicine, radiology, biometrics, forecasting of natural hazards, meteorology, model performance assessment, and other areas for many decades and is increasingly used in machine learning and data mining research. (wikipedia.org)
• ROCCET's ROC curve generation and analysis is specifically tailored for metabolomics datasets. (wikipedia.org)
• In the multivariate module one can choose between three different techniques - SVM (support vector machine), PLS-DA (partial least squares discriminant analysis) and Random Forests for classifying and selecting metabolites or clinical variables for an optimal ROC performance. (wikipedia.org)
• methods
• Smooth of a ROC curve (sometimes the classification is based on a discrete scale (e.g. for a cancer diagnostic tool: "normal", "benign", "probably benign", "suspicious", "malignant") and smoothing methods fit a continuous curve based on the assumptions of the background distribution). (smart-statistics.com)
• threshold
• The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. (wikipedia.org)
• diagnostic
• The index is defined for all points of an ROC curve, and the maximum value of the index may be used as a criterion for selecting the optimum cut-off point when a diagnostic test gives a numeric rather than a dichotomous result. (wikipedia.org)
• Figure
• The resulting ROC curves from Resnik's and Robinson's measures give better results than those provided by PhenomeNET (Figure 6A). (nih.gov)
• The ROC curve with CBG values as the screening variable and VPG values as the reference (positive actual state 7.0 mmol/L (126 mg/dl)) is shown in Figure 1 and the ROC curves with CBG and VPG as the screening variables and HbA1c as the reference (positive actual state 7%) are provided in Figure 2. (nih.gov)
• An image of different ROC curves is shown in Figure 1. (wikipedia.org)
• respectively
• The areasunder the ROC curve for TGF-β1 and MCP-1 were 0.69 (95% CI, 0.55-0.84,p=0.012) and 0.65 (95% CI, 0.50-0.81,p=0.048), respectively. (nih.gov)
• The ROC curves of Δ%MPV and Δ%PDW are shown in Figures 1 and 2 respectively. (nih.gov)
• A ROC space is defined by FPR and TPR as x and y axes, respectively, which depicts relative trade-offs between true positive (benefits) and false positive (costs). (wikipedia.org)
• values
• One feature of the rocsnr function is that you can specify a vector of SNR values and rocsnr calculates the ROC curve for each of these SNR values. (mathworks.com)
• equivalent
• An alternative approach is to define the Gini coefficient as half of the relative mean absolute difference, which is mathematically equivalent to the Lorenz curve definition. (wikipedia.org)
• test
• A ROC curve starts at the (0,0) coordinate representing the case when all test results are negative and ends at (1,1) coordinate, representing that all test results are positive. (smart-statistics.com)
• several
• A ROC curve was built using the same reference set of inferred relationships between OMIM diseases that share one or several genes. (nih.gov)