#the model predicted admitted and the student was admitted
true_positives = len(admissions[(admissions["predicted_label"] == 1) & (admissions["actual_label"] == 1)])
#the model predicted rejected but the student was actually admitted
false_negatives = len(admissions[(admissions["predicted_label"] == 0) & (admissions["actual_label"] == 1)])
sensitivity = true_positives / (true_positives+false_negatives )
The dataset is about graduate school admissions.
Sensitivity or True Postive Rate is the proportion of applicants that were correctly admitted of all of the students that should have been admitted (True Positives + False Negatives).
Sensitivity or True Postive Rate is the proportion of applicants that were correctly admitted of all of the students that should have been admitted (True Positives + False Negatives).
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