Question

我正在研究逻辑回归模型。我从两个单独的 CSV 文件开始，一个用于训练数据，另一个用于测试数据。我创建了两个单独的数据框，每个数据集一个。我能够很好地拟合和训练模型，但是当我尝试使用测试数据进行预测时出现错误。

我不确定我是否正确设置了 y_train 变量，或者是否存在其他问题。我在运行预测时收到以下错误消息。

这是模型的设置和代码"

#Setting x and y values
X_train = clean_df_train[['account_length','total_day_charge','total_eve_charge', 'total_night_charge', 
            'number_customer_service_calls']]
y_train = clean_df_train['churn']

X_test = clean_df_test[['account_length','total_day_charge','total_eve_charge', 'total_night_charge', 
            'number_customer_service_calls']]
y_test = clean_df_test['churn']

#Fitting / Training the Logistic Regression Model
logreg = LogisticRegression()
logreg.fit(X_train, y_train)

LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='warn',
          n_jobs=None, penalty='l2', random_state=None, solver='warn',
          tol=0.0001, verbose=0, warm_start=False)

#Make Predictions with Logit Model
predictions = logreg.predict(X_test)

#Measure Performance of the model
from sklearn.metrics import classification_report

#Measure performance of the model
classification_report(y_test, predictions)

  1522     """
   1523 
-> 1524     y_type, y_true, y_pred = _check_targets(y_true, y_pred)
   1525 
   1526     labels_given = True

E:\Users\davidwool\Anaconda3\lib\site-packages\sklearn\metrics\classification.py in _check_targets(y_true, y_pred)
     79     if len(y_type) > 1:
     80         raise ValueError("Classification metrics can't handle a mix of {0} "
---> 81                          "and {1} targets".format(type_true, type_pred))
     82 
     83     # We can't have more than one value on y_type => The set is no more needed

ValueError: Classification metrics can't handle a mix of continuous and binary targets

这是我正在处理的数据的头部。流失列完全空白，因为它是我试图预测的内容。

clean_df_test.head()

    account_length  total_day_charge    total_eve_charge    total_night_charge  number_customer_service_calls   churn
0               74             31.91               13.89                 8.82                               0     NaN
1               57             30.06               16.58                 9.61                               0     NaN
2              111             36.43               17.72                 8.21                               1     NaN
3               77             42.81               17.48                12.38                               2     NaN
4               36             47.84               17.19                 8.42                               2     NaN

这里还有 dtype。

clean_df_test.dtypes
account_length                     int64
total_day_charge                 float64
total_eve_charge                 float64
total_night_charge               float64
number_customer_service_calls      int64
churn                            float64
dtype: object

主要问题是我习惯于在一个数据集上使用 sklearn 的 train_test_split() 函数，这里我有 2 个单独的数据集，所以我不确定我的 y-test 应该设置为什么。

Answer 1

通过查看clean_df_test.head()，问题变得明显。我可以看到 churn 列中有空值。

因此，y_test 包含空值，通过将它作为 y_true 传递给 classification_report()，您正在使函数将空值与整数进行比较，从而引发错误。< /p>

要解决此问题，请尝试删除 churn 为 NaN 的行，然后像以前一样运行其余代码。

# Drop records where `churn` is NaN
clean_df_test.dropna(axis=0, subset=['churn'], inplace=True)

# Carry on as before
X_test = clean_df_test[['account_length','total_day_charge','total_eve_charge', 'total_night_charge', 
            'number_customer_service_calls']]
y_test = clean_df_test['churn']

发现此问题的另一种方法是查看 clean_df_test 的数据类型。从输出来看，churn 的类型是 float，如果它完全由 1 和 0 填充，则不应该是这种情况！

如何使用单独的 df 对训练和测试数据进行逻辑回归模型的预测

1 个答案: