Generate negative predictive value using cross_val_score in sklearn for model performance evaluation

Question

As part of evaluating a model's metrics, I would like to use cross_val_score in sklearn to generate negative predictive value for a binary classification model.

In example below, I set the 'scoring' parameter within cross_val_score to calculate and print 'precision' (mean and standard deviations from 10-fold cross-validation) for positive predictive value of the model:

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
log=LogisticRegression()    
log_prec = cross_val_score(log, x, y, cv=10, scoring='precision')
print("PPV(mean, std): ", np.round(log_prec.mean(), 2), np.round(log_prec.std(), 2))

How can I use something like the above line of code to generate negative predictive value/NPV (likelihood of a predicted negative to be a true negative) from within the cross_val_score method?

sklearn provides many scoring options (eg: roc_auc, recall, accuracy, F1, etc) but unforunately not one for NPV...

Answer 1

For a binary classification problem you can invert the label definition. Then the PPV will correspond to the NPV in you original problem