关于python：Scikit Learn – ValueError：操作数无法一起广播

Scikit Learn - ValueError: operands could not be broadcast together

我正试图在数据集上应用Gaussian Naive Bayes模型来预测疾病。当我使用训练数据进行预测时，它是正确运行的，但是当我尝试使用测试数据进行预测时，它给出了ValueError。

runfile('D:/ROFI/ML/Heart Disease/prediction.py', wdir='D:/ROFI/ML/Heart Disease')
Traceback (most recent call last):

File"", line 1, in
runfile('D:/ROFI/ML/Heart Disease/prediction.py', wdir='D:/ROFI/ML/Heart Disease')

File"C:\Users\User\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 866, in runfile
execfile(filename, namespace)

File"C:\Users\User\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)

File"D:/ROFI/ML/Heart Disease/prediction.py", line 85, in
predict(x_train, y_train, x_test, y_test)

File"D:/ROFI/ML/Heart Disease/prediction.py", line 73, in predict
predicted_data = model.predict(x_test)

File"C:\Users\User\Anaconda3\lib\site-packages\sklearn
aive_bayes.py", line 65, in predict
jll = self._joint_log_likelihood(X)

File"C:\Users\User\Anaconda3\lib\site-packages\sklearn
aive_bayes.py", line 429, in _joint_log_likelihood
n_ij -= 0.5 * np.sum(((X - self.theta_[i, :]) ** 2) /

ValueError: operands could not be broadcast together with shapes (294,14) (15,)

这里怎么了？

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import pandas
from sklearn import metrics
from sklearn.preprocessing import Imputer
from sklearn.naive_bayes import GaussianNB

def load_data(feature_columns, predicted_column):

train_data_frame = pandas.read_excel("training_data.xlsx")
test_data_frame = pandas.read_excel("testing_data.xlsx")
data_frame = pandas.read_excel("data_set.xlsx")

x_train = train_data_frame[feature_columns].values
y_train = train_data_frame[predicted_column].values

x_test = test_data_frame[feature_columns].values
y_test = test_data_frame[predicted_column].values

x_train, x_test = impute(x_train, x_test)

return x_train, y_train, x_test, y_test

def impute(x_train, x_test):

fill_missing = Imputer(missing_values=-9, strategy="mean", axis=0)

x_train = fill_missing.fit_transform(x_train)
x_test = fill_missing.fit_transform(x_test)

return x_train, x_test

def predict(x_train, y_train, x_test, y_test):

model = GaussianNB()
model.fit(x_train, y_train.ravel())

predicted_data = model.predict(x_test)
accuracy = metrics.accuracy_score(y_test, predicted_data)
print("Accuracy of our naive bayes model is : %.2f"%(accuracy * 100))

return predicted_data

feature_columns = ["age","sex","chol","cigs","years","fbs","trestbps","restecg","thalach","exang","oldpeak","slope","ca","thal","num"]
predicted_column = ["cp"]

x_train, y_train, x_test, y_test = load_data(feature_columns, predicted_column)

predict(x_train, y_train, x_test, y_test)

注意：两个文件的列数相同。