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 the 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...

Do you have a binary classification problem? If so, why don't you invert the label definition? Then the PPV will correspond to the NPV in you original problem. — Mischa Lisovyi
This is a clever work-around and helpful indeed! Please post this as your answer and I will accept. — sb2020

Mischa Lisovyi Mischa Lisovyi · Accepted Answer · 2019-04-15T20:30:50

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

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

1 Answers