Путаница с pos_label в Roc_curve ⇐ Python

[Гость]

У меня вопрос, связанный с

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roc_curve

from

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scikit-learn

for a deep learning exercise, I have noticed that my data has 1 as the positive label. After my training the testing accuracy is coming around 74% but the roc area under curve(AUC) score coming as only as .24.

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y_pred = model.predict([x_test_real[:, 0],x_test_real[:, 1]])
fpr, tpr, thresholds = metrics.roc_curve(y_test_real, y_pred,pos_label=1)
roc_auc = metrics.auc(fpr, tpr)
print("roc_auc:  %0.2f" % roc_auc)

If I change the

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pos_label

to 0. The auc score becomes 0.76(obviously)

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y_pred = model.predict([x_test_real[:, 0],x_test_real[:, 1]])
fpr, tpr, thresholds = metrics.roc_curve(y_test_real, y_pred,pos_label=0)
roc_auc = metrics.auc(fpr, tpr)
print("roc_auc:  %0.2f" % roc_auc)

Now I ran a small experiment, I changed my training and testing labels(which are binary classification)

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y_train_real = 1 - y_train_real
y_test_real = 1 - y_test_real

like this, which should flip the positive and negative labels from 1 to 0. Then I run my code again. This time expecting the behavior of the roc auc to flip as well. But NO!

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fpr, tpr, thresholds = metrics.roc_curve(y_test_real, y_pred,pos_label=0)

Is still giving .80 and with

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pos_label=1

is giving .2. This is confusing me,

• If I change the positive label in my training target should it not affect the roc_curve auc values?
• Which case is the correct analysis?
• Does the output have anything to do with the loss function used? I am solving a binary classification problem of match and not match using "contrastive loss".

Источник: https://stackoverflow.com/questions/512 ... -confusion