是否可以从独立列预测多个依赖列？

问题陈述： 我需要基于STATUS列预测5个因素（cEXT, cNEU, cAGR, cCON, cOPN），因此输入变量仅为STATUS列，目标变量为（cEXT, cNEU, cAGR, cCON, cOPN）。

在上述数据中，STATUS是独立列，而cEXT, cNEU, cAGR, cCON, cOPN是依赖列，如何预测这些列？

# 独立和依赖变量分割X = df[['STATUS']]y = df[["cEXT","cNEU","cAGR","cCON","cOPN"]]

目前我只能预测一个列，因此我重复了5次同样的操作，所以我为5个目标变量创建了5个模型。

代码：

X = df[['STATUS']]y = df[["cEXT","cNEU","cAGR","cCON","cOPN"]]from sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=5)from sklearn.compose import ColumnTransformerfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizerct = ColumnTransformer([    ('step1', TfidfVectorizer(), 'STATUS')],remainder='drop')from sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import accuracy_score, recall_score, classification_report, cohen_kappa_scorefrom sklearn import metrics from sklearn.pipeline import Pipeline# ########## # RandomForest# ##########model = Pipeline([        ('column_transformers', ct),        ('model', RandomForestClassifier(criterion = 'gini', n_estimators=100, n_jobs = -1, class_weight = 'balanced', max_features = 'auto')),    ])# 创建5个模型，我可以创建1个模型吗？model_cEXT = model.fit(X_train, y_train['cEXT'])model_cNEU = model.fit(X_train, y_train['cNEU'])model_cAGR = model.fit(X_train, y_train['cAGR'])model_cCON = model.fit(X_train, y_train['cCON'])model_cOPN = model.fit(X_train, y_train['cOPN'])

回答：

您可以使用scikit-learn的多输出分类器。

from sklearn.multioutput import MultiOutputClassifierfrom sklearn.ensemble import RandomForestClassifier

clf = MultiOutputClassifier(RandomForestClassifier()).fit(X_train, y_train)clf.predict(X_test)

参考资料：MultiOutputClassifier的官方文档