如果您能讓我知道如何使用 SMOTENC，我將不勝感激。我寫：# DataXX = pd.read_csv('Financial Distress.csv')y = np.array(XX['Financial Distress'].values.tolist())y = np.array([0 if i > -0.50 else 1 for i in y])Na = np.array(pd.read_csv('Na.csv', header=None).values)XX = XX.iloc[:, 3:127]# Use get-dummies to convert categorical features into dummy onesdis_features = ['x121']X = pd.get_dummies(XX, columns=dis_features)# # Divide Data into Train and Testindices = np.arange(y.shape[0])X_train, X_test, y_train, y_test, idx_train, idx_test = train_test_split(X, y, indices, stratify=y, test_size=0.3,                                                                         random_state=42)num_indices=list(X)[:X.shape[1]-37]cat_indices=list(X)[X.shape[1]-37:]num_indices1 = list(X.iloc[:,np.r_[0:94,95,97,100:123]].columns.values)cat_indices1 = list(X.iloc[:,np.r_[94,96,98,99,123:160]].columns.values)print(len(num_indices1))print(len(cat_indices1))pipeline=Pipeline(steps= [    # Categorical features    ('feature_processing', FeatureUnion(transformer_list = [            ('categorical', MultiColumn(cat_indices)),            #numeric            ('numeric', Pipeline(steps = [                ('select', MultiColumn(num_indices)),                ('scale', StandardScaler())                        ]))        ])),    ('clf', rg)    ])pipeline_with_resampling = make_pipeline(SMOTENC(categorical_features=cat_indices1), pipeline)# # Grid Search to determine best paramscv=StratifiedKFold(n_splits=5,random_state=42)rg_cv = GridSearchCV(pipeline_with_resampling, param_grid, cv=cv, scoring = 'f1')rg_cv.fit(X_train, y_train)因此，正如所指出的，我有 5 個分類特征。實際上，索引 123 到 160 與一個具有 37 個可能值的分類特征相關，這些特征使用 get_dummies 轉換為 37 列。