我正在嘗試運(yùn)行下面的代碼。import numpy as npimport matplotlib.pyplot as pltfrom sklearn.metrics import classification_reportfrom sklearn import cross_validationfrom sklearn.tree import DecisionTreeClassifierfrom utilities import visualize_classifier# Load input datainput_file = 'data_decision_trees.txt'data = np.loadtxt(input_file, delimiter=',')X, y = data[:, :-1], data[:, -1]# Separate input data into two classes based on labelsclass_0 = np.array(X[y==0])class_1 = np.array(X[y==1])# Visualize input dataplt.figure()plt.scatter(class_0[:, 0], class_0[:, 1], s=75, facecolors='black',         edgecolors='black', linewidth=1, marker='x')plt.scatter(class_1[:, 0], class_1[:, 1], s=75, facecolors='white',         edgecolors='black', linewidth=1, marker='o')plt.title('Input data')# Split data into training and testing datasets X_train, X_test, y_train, y_test = cross_validation.train_test_split(        X, y, test_size=0.25, random_state=5)# Decision Trees classifier params = {'random_state': 0, 'max_depth': 4}classifier = DecisionTreeClassifier(**params)classifier.fit(X_train, y_train)visualize_classifier(classifier, X_train, y_train, 'Training dataset')y_test_pred = classifier.predict(X_test)visualize_classifier(classifier, X_test, y_test, 'Test dataset')# Evaluate classifier performanceclass_names = ['Class-0', 'Class-1']print("\n" + "#"*40)print("\nClassifier performance on training dataset\n")print(classification_report(y_train, classifier.predict(X_train), target_names=class_names))print("#"*40 + "\n")print("#"*40)print("\nClassifier performance on test dataset\n")print(classification_report(y_test, y_test_pred, target_names=class_names))print("#"*40 + "\n")plt.show()錯(cuò)誤發(fā)生在這一行：from sklearn import cross_validation我在 Anaconda 命令窗口中嘗試了以下操作：conda update scikit-learnpip install -U scikit-learn在這兩種情況下，我都得到了“要求已經(jīng)是最新的”的確認(rèn)信息。我不確定為什么我在嘗試運(yùn)行腳本時(shí)會(huì)收到這些錯(cuò)誤消息，因?yàn)樗?sklearn 的東西似乎都是最新的。還有什么我可以嘗試的嗎？這段代碼肯定可以工作，但它會(huì)不斷地絆倒自己。謝謝。