我有一個代碼示例 - 具有兩個組件（PCA 和隨機森林）的 sklearn 管道，我想使用管道的中間結果以帶來一些可解釋性。我知道可以使用 .get_params() 來查看中間步驟，但是是否可以保存或提取中間結果以進行其他操作？我想應用 PCA 的附加功能（代碼中的 1.1 和 1.2 部分）from sklearn.datasets import load_breast_cancerimport numpy as npimport pandas as pdfrom sklearn.decomposition import FastICA, PCAfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.pipeline import Pipelinefrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import classification_reportfrom sklearn.metrics import confusion_matrix#Convert the dataset to data framecancer = load_breast_cancer()     data = np.c_[cancer.data, cancer.target]columns = np.append(cancer.feature_names, ["target"])df = pd.DataFrame(data, columns=columns)#Split data into train and test X = df.iloc[:, 0:30].valuesY = df.iloc[:, 30].valuesX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.25, random_state = 0)#Create a pipeline n_comp = 12clf = Pipeline([('pca', PCA(n_comp)), ('RandomForest', RandomForestClassifier(n_estimators=100))])clf.fit(X_train, Y_train)#Evalute the pipeline cr = classification_report(Y_test, Y_pred)print(cr)#see the intermediate steps of the pipelineprint(clf.get_params()['pca'])##1.1 if I create PCA outside of the pipeline pca = PCA(n_components=10)principalComponents = pca.fit_transform(X)##1.2 some explainability on pca outside of the pipeline pca.explained_variance_ratio_