Positive Predictive Value
Overview
The Positive Predictive Value (PPV), also known as Precision, is a metric used to evaluate the performance of a classification model in terms of the proportion of positive predictions that are correct. It measures the model's ability to correctly identify positive instances.
Formula
PPV = P(actual = + | prediction = +) = TP/(TP + FP)
Where:
- TP (True Positives): The number of positive instances correctly predicted by the model
- FP (False Positives): The number of negative instances incorrectly predicted as positive by the model
Usage
Manually
ppv_priv, ppv_unpriv = positive_predicted_value(df, protected_attribute, privileged_group, labels, positive_label, y_true)
print("Positive Predicted Value for privileged group:", ppv_priv)
print("Positive Predicted Value for unprivileged group:", ppv_unpriv)
Using Fairness Object
ppp_priv, ppv_unpriv = (fo.compute(positive_predicted_value))
Results
Positive Predicted Value for privileged group: 0.399999999992
Positive Predicted Value for unprivileged group: 0.399999999992
These results are obtained by using the input data given in the Create Example Data page under Getting Started
Interpretation
A higher PPV value indicates that the model has a higher proportion of correct positive predictions, which is desirable. A PPV of 1.0 indicates that all positive predictions made by the model are correct.
Updated 9 months ago