Scikit Learn - ValueError：操作数无法一起广播

我正在尝试在数据集上应用 Gaussian Naive Bayes 模型来预测疾病 . 它's running correctly when I'm预测使用训练数据，但是当我'm trying to predict using testing data It'给出 ValueError 时 .

runfile（'D：/ ROFI / ML / Heart Disease / prediction.py'，wdir ='D：/ ROFI / ML / Heart Disease'）Traceback（最近一次调用最后一次）：文件“”，第1行，在runfile中（'D：/ ROFI / ML /心脏病/ prediction.py'，wdir ='D：/ ROFI / ML /心脏病'）文件“C：\ Users \ User \ Anaconda3 \ lib \ site-packages \ spyder \ utils \ site \ sitecustomize.py“，第866行，在runfile execfile（文件名，命名空间）文件”C：\ Users \ User \ Anaconda3 \ lib \ site-packages \ spyder \ utils \ site \ sitecustomize.py“，第102行，在execfile exec（compile（f.read（），filename，'exec'），namespace）文件“D：/ ROFI / ML / Heart Disease / prediction.py”，第85行，在predict（x_train，y_train，x_test） ，y_test）文件“D：/ ROFI / ML / Heart Disease / prediction.py”，第73行，预测predict_data = model.predict（x_test）文件“C：\ Users \ User \ Anaconda3 \ lib \ site-packages \ sklearn \ naive_bayes.py“，第65行，预测jll = self._joint_log_likelihood（X）文件”C：\ Users \ User \ Anaconda3 \ lib \ site-packages \ sklearn \ naive_bayes.py“，第429行，_joint_log_likelihood n_ij - = 0.5 * np.sum（（（X - self.theta _ [i，：]）** 2）/ ValueError：操作数无法与形状一起广播（294,14）（15，）

这有什么不对？

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)

N.B：两个文件的列数相同 .