Computer scienceData scienceMachine learningClassificationClassification metrics

ROC and AUC

Decision thresholds

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Suppose you have the following list of probabilities from your model :

[0.75, 0.82, 0.32, 0.64, 0.81, 0.21, 0.47, 0.2, 0.19, 0.11]

The actual labels are:

[1, 1, 1, 1, 1, 0, 0, 0, 0, 0]

You'd like to build a ROC curve now. For starters, you took a random decision threshold, let's say - $0.34$ . Calculate the true positive rate for this threshold.

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