DNN 分类后出现以下报错：
import scipy.sparse
xtest_count=scipy.sparse.lil_matrix(xtest_count).toarray()
ytest_count = scipy.sparse.lil_matrix(ytest_count).toarray()
predictions = model.predict(xtest_count, batch_size=512)
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
import pandas as pd
cm = confusion_matrix(ytest_count, predictions)
cm_df = pd.DataFrame(cm.T, index=encoder.classes_, columns=encoder.classes_)
cm_df.index.name = 'Predicted'
cm_df.columns.name = 'True'
print(cm_df)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-29-7ec755bc0a87> in <module>()
1 from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
2 import pandas as pd
----> 3 cm = confusion_matrix(ytest_count, predictions)
4 cm_df = pd.DataFrame(cm.T, index=encoder.classes_, columns=encoder.classes_)
5 cm_df.index.name = 'Predicted'
1 frames
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py in _check_targets(y_true, y_pred)
93 raise ValueError(
94 "Classification metrics can't handle a mix of {0} and {1} targets".format(
---> 95 type_true, type_pred
96 )
97 )
ValueError: Classification metrics can't handle a mix of multilabel-indicator and continuous-multioutput targets
请大家帮我看看！谢谢！
sklearn的classification metrics只接受binary的targets，所以你需要确保ytest_count,和predictions中的元素都是0或1的array。
您好，我是有问必答小助手，您的问题已经有小伙伴帮您解答，感谢您对有问必答的支持与关注！