在 sklearn 中使用 cross_val_score 生成负预测值用于模型性能评估

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.

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))

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

Answer 2

You can use make_scorer<\/code><\/a> to pass in pos_label=0<\/code> to the precision score function ( metrics.precision_score<\/code><\/a> ) to get NPV. Like this:

from sklearn.metrics import make_scorer, precision_score
npv = cross_val_score(log, x, y, cv=10, scoring=make_scorer(precision_score, pos_label=0))