我的代码有什么问题，它在使用朴素贝叶斯分类器对工作进行分类时返回错误的预测

Question

这就是我训练模型的方式。我已经使用 train_test_split 拆分了数据。

G_clf = GaussianNB()
G_clf.fit(X_train, y_train)

M_clf = MultinomialNB()
M_clf.fit(X_train, y_train)

B_clf = BernoulliNB(alpha=0.1, fit_prior=True, binarize=0.5)
B_clf.fit(X_train, y_train)

然后把它扔到泡菜里

with open('nbB_model.pkl', 'wb') as f:
    pickle.dump(B_clf, f)
nbB_model_dump = pickle.load(open('nbB_model.pkl', 'rb'))

with open('nbG_model.pkl', 'wb') as g:
    pickle.dump(G_clf, g)
nbG_model_dump = pickle.load(open('nbG_model.pkl', 'rb'))

with open('nbM_model.pkl', 'wb') as h:
    pickle.dump(M_clf, h)
nbM_model_dump = pickle.load(open('nbM_model.pkl', 'rb'))

这是根据输入的薪水和经验预测工作的代码

job_map = {
    1: 'Junior',
    2: 'Senior',
    3: 'Project Manager',
    4: 'CTO',
}

def predictnaive():
# Get the user's input values
salary = float(request.form['salary'])
experience = float(request.form['experience'])

# Use each Naive Bayes model to make a prediction based on the user's input values
gaussian_prediction = gaussian_nb.predict([[salary, experience]])[0]
multinomial_prediction = multinomial_nb.predict([[salary, experience]])[0]
bernoulli_prediction = bernoulli_nb.predict([[salary, experience]])[0]

# Map the predicted job titles to their corresponding string values
gaussian_prediction = job_map.get(gaussian_prediction)
multinomial_prediction = job_map.get(multinomial_prediction)
bernoulli_prediction = job_map.get(bernoulli_prediction)