#the model predicted rejected and the student was rejected
true_negatives = len(admissions[(admissions["predicted_label"] == 0) & (admissions["actual_label"] == 0)])
#the model predicted admitted but the student was actually rejected
false_positives = len(admissions[(admissions["predicted_label"] == 1) & (admissions["actual_label"] == 0)])
specificity= true_negatives / (true_negatives + false_positives )
The dataset is about graduate school admissions.
Specificity or True Negative Rate is the proportion of applicants that were correctly rejected.
Specificity or True Negative Rate is the proportion of applicants that were correctly rejected.
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