我正在尝试在一个包含数值、分类和文本数据的数据集上训练一个LightGBM模型。然而，在训练阶段，我遇到了以下错误：
params = {'num_class':5,'max_depth':8,'num_leaves':200,'learning_rate': 0.05,'n_estimators':500}clf = LGBMClassifier(params)data_processor = ColumnTransformer([ ('numerical_processing', numerical_processor, numerical_features), ('categorical_processing', categorical_processor, categorical_features), ('text_processing_0', text_processor_1, text_features[0]), ('text_processing_1', text_processor_1, text_features[1]) ]) pipeline = Pipeline([ ('data_processing', data_processor), ('lgbm', clf) ])pipeline.fit(X_train, y_train)错误信息如下：
TypeError: Unknown type of parameter:boosting_type, got:dict这是我的pipeline：
我有两个文本特征，主要是对一些形式的名称进行词干提取。
任何建议都会非常感激。
回答：
您设置分类器的方式是错误的，这导致了错误，您可以在进入pipeline之前先尝试一下：
params = {'num_class':5,'max_depth':8,'num_leaves':200,'learning_rate': 0.05,'n_estimators':500}clf = LGBMClassifier(params)clf.fit(np.random.uniform(0,1,(50,2)),np.random.randint(0,5,50))同样会得到错误：
TypeError: Unknown type of parameter:boosting_type, got:dict您可以这样设置分类器：
clf = LGBMClassifier(**params)然后使用一个例子，您可以看到它可以运行：
from sklearn.pipeline import Pipelinefrom sklearn.preprocessing import StandardScaler, OneHotEncoderfrom sklearn.compose import ColumnTransformernumerical_processor = StandardScaler()categorical_processor = OneHotEncoder()numerical_features = ['A']categorical_features = ['B']data_processor = ColumnTransformer([('numerical_processing', numerical_processor, numerical_features),('categorical_processing', categorical_processor, categorical_features)])X_train = pd.DataFrame({'A':np.random.uniform(100),'B':np.random.choice(['j','k'],100)})y_train = np.random.randint(0,5,100)pipeline = Pipeline([('data_processing', data_processor),('lgbm', clf)])pipeline.fit(X_train, y_train)