K折验证 The least populated class in y has only 1 members, which is less than n_splits=10_userwarning: the least populated class in y has on-优快云博客

UserWarning: The least populated class in y has only 1 members, which is less than n_splits=10

D:\nlp\ENVS\env39\lib\site-packages\sklearn\model_selection\_split.py:666: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=10.
warnings.warn(("The least populated class in y has only %d"
D:\nlp\ENVS\env39\lib\site-packages\sklearn\model_selection\_validation.py:696: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\model_selection\_validation.py", line 687, in _score
scores = scorer(estimator, X_test, y_test)
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\metrics\_scorer.py", line 199, in __call__
return self._score(partial(_cached_call, None), estimator, X, y_true,
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\metrics\_scorer.py", line 288, in _score
return self._sign * self._score_func(y, y_pred, **self._kwargs)
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\metrics\_classification.py", line 2269, in log_loss
raise ValueError("y_true and y_pred contain different number of "
ValueError: y_true and y_pred contain different number of classes 9, 15. Please provide the true labels explicitly through the labels argument. Classes found in y_true: [ 0 2 4 5 6 9 11 12 14]

warnings.warn(
D:\nlp\ENVS\env39\lib\site-packages\sklearn\model_selection\_validation.py:696: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\model_selection\_validation.py", line 687, in _score
scores = scorer(estimator, X_test, y_test)
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\metrics\_scorer.py", line 199, in __call__
return self._score(partial(_cached_call, None), estimator, X, y_true,
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\metrics\_scorer.py", line 288, in _score
return self._sign * self._score_func(y, y_pred, **self._kwargs)
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\metrics\_classification.py", line 2269, in log_loss
raise ValueError("y_true and y_pred contain different number of "
ValueError: y_true and y_pred contain different number of classes 9, 15. Please provide the true labels explicitly through the labels argument. Classes found in y_true: [ 0 2 4 5 6 9 11 12 15]

warnings.warn(
D:\nlp\ENVS\env39\lib\site-packages\sklearn\model_selection\_validation.py:696: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\model_selection\_validation.py", line 687, in _score
scores = scorer(estimator, X_test, y_test)
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\metrics\_scorer.py", line 199, in __call__
return self._score(partial(_cached_call, None), estimator, X, y_true,
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\metrics\_scorer.py", line 288, in _score
return self._sign * self._score_func(y, y_pred, **self._kwargs)
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\metrics\_classification.py", line 2269, in log_loss
raise ValueError("y_true and y_pred contain different number of "
ValueError: y_true and y_pred contain different number of classes 8, 16. Please provide the true labels explicitly through the labels argument. Classes found in y_true: [ 0 2 4 5 6 9 11 12]

warnings.warn(
D:\nlp\ENVS\env39\lib\site-packages\sklearn\model_selection\_validation.py:696: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\model_selection\_validation.py", line 687, in _score
scores = scorer(estimator, X_test, y_test)
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\metrics\_scorer.py", line 199, in __call__
return self._score(partial(_cached_call, None), estimator, X, y_true,
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\metrics\_scorer.py", line 288, in _score
return self._sign * self._score_func(y, y_pred, **self._kwargs)
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\metrics\_classification.py", line 2269, in log_loss
raise ValueError("y_true and y_pred contain different number of "
ValueError: y_true and y_pred contain different number of classes 10, 14. Please provide the true labels explicitly through the labels argument. Classes found in y_true: [ 0 2 4 5 6 8 9 11 12 13]

warnings.warn(
D:\nlp\ENVS\env39\lib\site-packages\sklearn\model_selection\_validation.py:696: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\model_selection\_validation.py", line 687, in _score
scores = scorer(estimator, X_test, y_test)
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\metrics\_scorer.py", line 199, in __call__
return self._score(partial(_cached_call, None), estimator, X, y_true,
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\metrics\_scorer.py", line 288, in _score
return self._sign * self._score_func(y, y_pred, **self._kwargs)
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "D:\nlp\ENVS\env39\lib\site-packages\sklearn\metrics\_classification.py", line 2269, in log_loss
raise ValueError("y_true and y_pred contain different number of "
ValueError: y_true and y_pred contain different number of classes 8, 16. Please provide the true labels explicitly through the labels argument. Classes found in y_true: [ 0 2 4 5 6 9 11 12]