如何从多个文本列中正确提取特征并应用任何分类算法？请指导我，如果我做错了什么

示例数据集

自变量： Description1,Description2, State, NumericCol1,NumericCol2

因变量： TargetCategory

代码：

########### Feature Extraction for Text Data ############################## Description1 (it can be any wordembedding technique like countvectorizer, tfidf, word2vec,bert..etc)tfidf = TfidfVectorizer(max_features = 500,                               ngram_range = (1,3),                              stop_words = "english")X_Description1 = tfidf.fit_transform(df["Description1"].tolist())######### Description2 (it can be any wordembedding technique like countvectorizer, tfidf, word2vec,bert..etc)tfidf = TfidfVectorizer(max_features = 500,                               ngram_range = (1,3),                              stop_words = "english")X_Description2 = tfidf.fit_transform(df["Description2"].tolist())######### State (have 100 unique entries thats why used BinaryEncoder)import category_encoders as cebinary_encoder= ce.BinaryEncoder(cols=['state'],return_df=True)X_state = binary_encoder.fit_transform(df["state"])import scipyX = scipy.sparse.hstack((X_Description1,                          X_Description2,                         X_state,                         df[["NumericCol1", "NumericCol2"]].to_numpy())).tocsr()y = df['TargetCategory']##### train Test Split ########from sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, random_state=111)##### Create Model Model ######from sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import accuracy_score, recall_score, classification_report, cohen_kappa_scorefrom sklearn import metrics # Baseline Random forest based Modelrfc = RandomForestClassifier(criterion = 'gini', n_estimators=1000, verbose=1, n_jobs = -1,                              class_weight = 'balanced', max_features = 'auto')rfcg = rfc.fit(X_train,y_train) # fit on training data####### Prediction ##########predictions = rfcg.predict(X_test)print('Baseline: Accuracy: ', round(accuracy_score(y_test, predictions)*100, 2))print('\n Classification Report:\n', classification_report(y_test,predictions))

回答：

在scikit-learn中使用多列作为输入的方法是使用ColumnTransformer。

这里有一个如何在异构数据上使用它的示例。