ظهور الخطأ التالي أثناء قياس كفاءة النموذج ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

قمت ببناء مودل LogisticRegression لكن عند محاولة قياس الكفاءة يظهر لي الخطأالتالي:

import numpy as np
from tensorflow.keras.datasets import mnist
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from tensorflow.keras.utils import to_categorical
from sklearn.metrics import f1_score,precision_score,recall_score,accuracy_score,log_loss
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train.shape
image_size = x_train.shape[1]
input_size = image_size * image_size
x_train = np.reshape(x_train, [-1, input_size])/ 255
x_test = np.reshape(x_test, [-1, input_size]) / 255
#y_train = to_categorical(y_train)
#y_test = to_categorical(y_test)
t =LogisticRegression()
t.fit(x_train, y_train)
f1_score(y_test,t.predict(x_test))
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-1-bbb2bd2287ff> in <module>
     15 t =LogisticRegression()
     16 t.fit(x_train, y_train)
---> 17 f1_score(y_test,t.predict(x_test))
     18 
     19 #ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

~\anaconda3\lib\site-packages\sklearn\metrics\_classification.py in f1_score(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)
   1097                        pos_label=pos_label, average=average,
   1098                        sample_weight=sample_weight,
-> 1099                        zero_division=zero_division)
   1100 
   1101 

~\anaconda3\lib\site-packages\sklearn\metrics\_classification.py in fbeta_score(y_true, y_pred, beta, labels, pos_label, average, sample_weight, zero_division)
   1224                                                  warn_for=('f-score',),
   1225                                                  sample_weight=sample_weight,
-> 1226                                                  zero_division=zero_division)
   1227     return f
   1228 

~\anaconda3\lib\site-packages\sklearn\metrics\_classification.py in precision_recall_fscore_support(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)
   1482         raise ValueError("beta should be >=0 in the F-beta score")
   1483     labels = _check_set_wise_labels(y_true, y_pred, average, labels,
-> 1484                                     pos_label)
   1485 
   1486     # Calculate tp_sum, pred_sum, true_sum ###

~\anaconda3\lib\site-packages\sklearn\metrics\_classification.py in _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
   1314             raise ValueError("Target is %s but average='binary'. Please "
   1315                              "choose another average setting, one of %r."
-> 1316                              % (y_type, average_options))
   1317     elif pos_label not in (None, 1):
   1318         warnings.warn("Note that pos_label (set to %r) is ignored when "

ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

ما الحل؟