如何将特异性定义为模型评估的可召集得分手

本文介绍了如何将特异性定义为模型评估的可召集得分手的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我正在使用此代码比较多种型号的性能:

I am using this code to compare performance of a number of models:

from sklearn import model_selection

X = input data
Y = binary labels

models = []
models.append(('LR', LogisticRegression()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))

results = []
names = []
scoring = 'accuracy'

for name, model in models:
    kfold = model_selection.KFold(n_splits=10, random_state=7)
    cv_results = model_selection.cross_val_score(model, X, Y, cv=kfold,scoring=scoring)
    results.append(cv_results)
    names.append(name)
    msg = "%s: %.2f (%.2f)" % (name, cv_results.mean(), cv_results.std())
    print(msg)

我可以使用准确性"和召回率"作为评分方式，这些都会提高准确性和敏感性.我该如何创建一个赋予我特异性"的得分手

I can use 'accuracy' and 'recall' as scoring and these will give accuracy and sensitivity. How can I create a scorer that gives me 'specificity'

特异性= TN/(TN+FP)

Specificity= TN/(TN+FP)

其中TN和FP在混淆矩阵中分别为真负值和假正值

where TN, and FP are true negative and false positive values in the confusion matrix

我已经尝试过了

def tp(y_true, y_pred): 
error= confusion_matrix(y_true, y_pred)[0,0]/(confusion_matrix(y_true,y_pred)[0,0] + confusion_matrix(y_true, y_pred)[0,1])
return error

my_scorer = make_scorer(tp, greater_is_better=True)

然后

cv_results = model_selection.cross_val_score(model, X,Y,cv=kfold,scoring=my_scorer)

，但不适用于n_split> = 10我在计算my_scorer时遇到此错误

but it will not work for n_split >=10I get this error for calculation of my_scorer

IndexError:索引1超出了尺寸1的轴1的范围

IndexError: index 1 is out of bounds for axis 1 with size 1

推荐答案

如果将二进制分类器的 recall_score 参数更改为 pos_label = 0 ，您将获得专一性(默认是灵敏度， pos_label = 1 )

If you change the recall_score parameters for a binary classifier to pos_label=0 you get specificity (default is sensitivity, pos_label=1)

scoring = {
    'accuracy': make_scorer(accuracy_score),
    'sensitivity': make_scorer(recall_score),
    'specificity': make_scorer(recall_score,pos_label=0)
}

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tn