在新的（未见过的）文本上部署文本分类模型

Question

我正在研究文本分类问题。我附上了我训练过的文本分类模型的简单虚拟片段。如何在 new_text 上部署模型？当模型用于 时check_predictions，它可以正确地对文本进行分类，但是，当使用新数据时，分类是错误的。这是因为new_text需要矢量化吗？我错过了一些基本的东西吗？from collections import Counterfrom sklearn.naive_bayes import MultinomialNBfrom sklearn.metrics import accuracy_scoreimport pandas as pdfrom sklearn.feature_extraction.text import CountVectorizerfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import classification_reportfrom sklearn.metrics import accuracy_score, precision_score, recall_scoredf = pd.read_csv("/Users/veg.csv")print (df)X_train, X_test, y_train, y_test = train_test_split(df['Text'], df['Label'],random_state=1, test_size=0.2)cv = CountVectorizer()X_train_vectorized = cv.fit_transform(X_train)X_test_vectorized = cv.transform(X_test)naive_bayes = MultinomialNB()naive_bayes.fit(X_train_vectorized, y_train)predictions = naive_bayes.predict(X_test_vectorized)print("Accuracy score: ", accuracy_score(y_test, predictions))print('accuracy %s' % accuracy_score(predictions, y_test))print(classification_report(y_test, predictions))check_predictions = []for i in range(len(X_test)):       if predictions[i] == 0:        check_predictions.append('vegetable')    if predictions[i] == 1:        check_predictions.append('fruit')    if predictions[i] == 2:        check_predictions.append('tree')        dummy_df = pd.DataFrame({'actual_label': list(y_test), 'prediction': check_predictions, 'Text':list(X_test)})dummy_df.replace(to_replace=0, value='vegetable', inplace=True)dummy_df.replace(to_replace=1, value='fruit', inplace=True)dummy_df.replace(to_replace=2, value='tree', inplace=True)print("DUMMY DF")print(dummy_df.head(10))

Answer 1

无论您输入模型中的任何（新）文本都必须经过与训练数据完全相同的预处理步骤 - 这里 CountVectorizer已经与您的X_train：

new_data_vectorized = cv.transform(new_data) # NOT fit_transform

new_predictions = naive_bayes.predict(new_data_vectorized)