How to Find AUC for the 6th Grade
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Step-by-step explanation:
The Receiver Operating Characetristic (ROC) curve is a graphical plot that allows us to assess the performance of binary classifiers. With imbalanced datasets, the Area Under the Curve (AUC) score is calculated from ROC and is a very useful metric in imbalanced datasets.
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Answer:
The AUC for the ROC can be calculated using the roc_auc_score() function. Like the roc_curve() function, the AUC function takes both the true outcomes (0,1) from the test set and the predicted probabilities for the 1 class. It returns the AUC score between 0.0 and 1.0 for no skill and perfect skill respectively.
A receiver operating characteristic curve, or ROC curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. The ROC curve is created by plotting the true positive rate against the false positive rate at various threshold settings.
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