ROC
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The ROC curve (Receiver Operating Characteristic curve) is a tool for evaluating the performance of classification models. It displays the classification ability of the model by plotting the relationship between the True Positive Rate (TPR) and the False Positive Rate (FPR). The horizontal axis of the ROC curve represents the False Positive Rate, while the vertical axis represents the True Positive Rate.
Input
Output
Chart Description
Chart Type: ROC curve (Receiver Operating Characteristic curve)
X-axis: False Positive Rate (1 - Specificity)
Y-axis: True Positive Rate (Sensitivity)
Red Curve: Represents the classification performance of the selected variable.
AUC Value: The chart displays the AUC (Area Under Curve) value.
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An AUC closer to 1 indicates stronger discriminative ability.
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An AUC of 0.5 suggests discriminative ability equivalent to random guessing.
The ROC curve (Receiver Operating Characteristic curve) is a tool for evaluating the performance of classification models. It displays the classification ability of the model by plotting the relationship between the True Positive Rate (TPR) and the False Positive Rate (FPR). The horizontal axis of the ROC curve represents the False Positive Rate, while the vertical axis represents the True Positive Rate.
Input
Output
Chart Description
Chart Type: ROC curve (Receiver Operating Characteristic curve)
X-axis: False Positive Rate (1 - Specificity)
Y-axis: True Positive Rate (Sensitivity)
Red Curve: Represents the classification performance of the selected variable.
AUC Value: The chart displays the AUC (Area Under Curve) value.
-
An AUC closer to 1 indicates stronger discriminative ability.
-
An AUC of 0.5 suggests discriminative ability equivalent to random guessing.