Question

我为这个幼稚的问题表示歉意，我已经在python中训练了一个模型（朴素贝叶斯），效果很好（95％的准确性）。它需要一个输入字符串（即“ Apple Inc.”或“ John Doe”），并识别出它是公司名称还是客户名称。

我实际上如何在另一个数据集上实现它？如果引入另一个熊猫数据框，如何将模型从训练数据中学到的内容应用于新数据框？

新数据框具有一个全新的填充和字符串集，它需要用来预测它是公司名称还是客户名称。

理想情况下，我想在新数据框中插入具有模型预测的列。

任何代码片段都值得赞赏。

当前模型的示例代码：

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(df["CUST_NM_CLEAN"], 
                                                    df["LABEL"],test_size=0.20, 
                                                    random_state=1)

# Instantiate the CountVectorizer method
count_vector = CountVectorizer()

# Fit the training data and then return the matrix
training_data = count_vector.fit_transform(X_train)

# Transform testing data and return the matrix. 
testing_data = count_vector.transform(X_test)

#in this case we try multinomial, there are two other methods
from sklearn.naive_bayes import cNB
naive_bayes = MultinomialNB()
naive_bayes.fit(training_data,y_train)
#MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)

predictions = naive_bayes.predict(testing_data)


from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
print('Accuracy score: {}'.format(accuracy_score(y_test, predictions)))
print('Precision score: {}'.format(precision_score(y_test, predictions, pos_label='Org')))
print('Recall score: {}'.format(recall_score(y_test, predictions, pos_label='Org')))
print('F1 score: {}'.format(f1_score(y_test, predictions, pos_label='Org')))

Answer 1

想通了。

# Convert a collection of text documents to a vector of term/token counts. 
cnt_vect_for_new_data = count_vector.transform(df['new_data'])

#RUN Prediction
df['NEW_DATA_PREDICTION'] = naive_bayes.predict(cnt_vect_for_new_data)

训练后如何应用ML模型？

1 个答案: