构建逻辑回归模型时出现AttributeError: 'str' object has no attribute 'decode'错误。

Question

我正在尝试构建一个逻辑回归模型，但出现了 AttributeError: 'str' object has no attribute 'decode' 的错误。请帮我修复一下。这段代码在Datacamp的服务器上运行得很好，但在我的电脑上出现了 AttributeError 错误。

import pandas as pd
df = pd.read_csv('datasets/diabetes.csv')
X = df.drop('diabetes',axis = 1)
y = df['diabetes']

# Import the necessary modules
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix

# Create training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state=42)

# Create the classifier: logreg
logreg = LogisticRegression()

# Fit the classifier to the training data
logreg.fit(X_train,y_train)

错误信息：

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-8-c8cf98ee145a> in <module>
     16 
     17 # Fit the classifier to the training data
---> 18 logreg.fit(X_train,y_train)
     19 
     20 #Predict the labels of the test set: y_pred

~\anaconda3\envs\tensorflow\lib\site-packages\sklearn\linear_model\_logistic.py in fit(self, X, y, 
sample_weight)
   1405         else:
   1406             prefer = 'processes'
-> 1407         fold_coefs_ = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
   1408                                **_joblib_parallel_args(prefer=prefer))(
   1409             path_func(X, y, pos_class=class_, Cs=[C_],


~\anaconda3\envs\tensorflow\lib\site-packages\sklearn\utils\optimize.py in 
_check_optimize_result(solver, result, max_iter, extra_warning_msg)
    241                 "    https://scikit-learn.org/stable/modules/"
    242                 "preprocessing.html"
--> 243             ).format(solver, result.status, result.message.decode("latin1"))
    244             if extra_warning_msg is not None:
    245                 warning_msg += "\n" + extra_warning_msg

 AttributeError: 'str' object has no attribute 'decode'

任何建议都将不胜感激。

- Bickky Sahani

Answer 1

看起来这是与scikit-learn版本有关的问题。

无论如何，在最新的scikit-learn版本（现在是0.24.1）中，通过将一部分代码置于try-catch块中，已经解决了这个问题。更多细节可在stackoverflow的问题中由进行了解释。

可能您使用的版本比较旧，所以我建议您使用以下代码升级scikit-learn库：

pip install -U scikit-learn

然后重新启动内核，检查新版本是否正确更新，并再次运行代码。

- Alex Serra Marrugat