我在尝试对数据集应用Gaussian Naive Bayes模型来预测疾病。当使用训练数据进行预测时运行正常，但当我尝试使用测试数据进行预测时，它会返回ValueError错误。

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\naive_bayes.py”, line 65, in predict jll = self._joint_log_likelihood(X)

File “C:\Users\User\Anaconda3\lib\site-packages\sklearn\naive_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,)

这里出了什么问题？

import pandasfrom sklearn import metricsfrom sklearn.preprocessing import Imputerfrom 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_testdef 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_testdef 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_datapredicted_column = ["cp"]feature_columns = ["age", "sex", "chol", "cigs", "years", "fbs", "trestbps", "restecg", "thalach", "exang", "oldpeak", "slope", "ca", "thal", "num"]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。

回答：

我找到了错误。错误是由Imputer引起的。Imputer用于替换数据集中的缺失值。但是，如果某列完全由缺失值组成，它会删除该列。我的测试数据集中有一列完全是缺失值。因此，Imputer删除了该列，导致形状与训练数据不匹配，这就是错误的原因。我从feature_columns列表中移除了完全由缺失值组成的列名，问题就解决了。