我有各种客户属性（自我描述和年龄）的数据，以及这些客户是否会购买特定产品的二元结果
{"would_buy": "No",
"self_description": "I'm a college student studying biology",
"Age": 19},
我想在MultinomialNB上使用self-description来预测would_buy，然后将这些预测结合到would_buy的逻辑回归模型中，该模型也需要将age作为协变量。
到目前为止的文本模型代码（我是SciKit的新手！），带有简化的数据集。
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
#Customer data that includes whether a customer would buy an item (what I'm interested), their self-description, and their age.
data = [
{"would_buy": "No", "self_description": "I'm a college student studying biology", "Age": 19},
{"would_buy": "Yes", "self_description": "I'm a blue-collar worker", "Age": 20},
{"would_buy": "No", "self_description": "I'm a Stack Overflow denzien", "Age": 56},
{"would_buy": "No", "self_description": "I'm a college student studying economics", "Age": 20},
{"would_buy": "Yes", "self_description": "I'm a UPS worker", "Age": 35},
{"would_buy": "No", "self_description": "I'm a Stack Overflow denzien", "Age": 56}
]
def naive_bayes_model(customer_data):
self_descriptions = [customer['self_description'] for customer in customer_data]
decisions = [customer['would_buy'] for customer in customer_data]
vectorizer = TfidfVectorizer(stop_words='english', ngram_range=(1,2))
X = vectorizer.fit_transform(self_descriptions, decisions)
naive_bayes = MultinomialNB(alpha=0.01)
naive_bayes.fit(X, decisions)
train(naive_bayes, X, decisions)
def train(classifier, X, y):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=22)
classifier.fit(X_train, y_train)
print(classification_report(classifier.predict(X_test), y_test))
def main():
naive_bayes_model(data)
main()
简短的回答是在训练有素的predict_proba上使用predict_log_proba或naive_bayes方法为逻辑回归模型创建输入。这些可以与Age值连接，以创建LogisticRegression模型的训练和测试集。
但是，我想指出您编写的代码在训练后无法访问您的naive_bayes模型。所以你肯定需要重构你的代码。
抛开这个问题，这就是我将naive_bayes的输出合并到LogisticRegression中的方法：
descriptions = np.array([customer['self_description'] for customer in data])
decisions = np.array([customer['would_buy'] for customer in data])
ages = np.array([customer['Age'] for customer in data])
vectorizer = TfidfVectorizer(stop_words='english', ngram_range=(1,2))
desc_vec = vectorizer.fit_transform(descriptions, decisions)
naive_bayes = MultinomialNB(alpha=0.01)
desc_train, desc_test, age_train, age_test, dec_train, dec_test = train_test_split(desc_vec, ages, decisions, test_size=0.25, random_state=22)
naive_bayes.fit(desc_train, dec_train)
nb_train_preds = naive_bayes.predict_proba(desc_train)
lr = LogisticRegression()
lr_X_train = np.hstack((nb_tarin_preds, age_train.reshape(-1, 1)))
lr.fit(lr_X_train, dec_train)
lr_X_test = np.hstack((naive_bayes.predict_proba(desc_test), age_test.reshape(-1, 1)))
lr.score(lr_X_test, dec_test)