我正在尝试使用这个来自sklearn文档的示例。我不太确定代码在做什么，尽管我认为我输入数据集的方式可能不对，但最近我遇到了这个错误：
<ipython-input-26-3c3c0763766b> in <module>() 49 for ds in datasets: 50 # preprocess dataset, split into training and test part---> 51 X, y = ds 52 X = StandardScaler().fit_transform(X) 53 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)ValueError: 要解包的值过多您有什么建议可以让我修改代码以适应我的数据集（这是一个来自pandas数据框的多维numpy数组）并修复这个错误吗？
dataURL = "peridotites_clean_complete.csv"pd_data = pd.read_csv(dataURL)rock_names = pd_data['ROCK NAME']rock_compositions = pd_data.columns[1:]rock_data = np.vstack([pd_data[x] for x in rock_compositions])classifiers = [ KNeighborsClassifier(3), SVC(kernel="linear", C=0.025), SVC(gamma=2, C=1), DecisionTreeClassifier(max_depth=5), RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1), AdaBoostClassifier(), GaussianNB(), LDA(), QDA()]X, y = make_classification(n_features=2, n_redundant=0, n_informative=2, random_state=1, n_clusters_per_class=1)rng = np.random.RandomState(2)X += 2 * rng.uniform(size=X.shape)linearly_separable = (X, y)datasets = [rock_data]figure = plt.figure(figsize=(27, 9))i = 1# iterate over datasetsfor ds in datasets: # preprocess dataset, split into training and test part X, y = ds X = StandardScaler().fit_transform(X) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4) x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5 y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # just plot the dataset first cm = plt.cm.RdBu cm_bright = ListedColormap(['#FF0000', '#0000FF']) ax = plt.subplot(len(datasets), len(classifiers) + 1, i) # Plot the training points ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright) # and testing points ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6) ax.set_xlim(xx.min(), xx.max()) ax.set_ylim(yy.min(), yy.max()) ax.set_xticks(()) ax.set_yticks(()) i += 1 # iterate over classifiers for name, clf in zip(names, classifiers): ax = plt.subplot(len(datasets), len(classifiers) + 1, i) clf.fit(X_train, y_train) score = clf.score(X_test, y_test) # Plot the decision boundary. For that, we will assign a color to each # point in the mesh [x_min, m_max]x[y_min, y_max]. if hasattr(clf, "decision_function"): Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) else: Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] # Put the result into a color plot Z = Z.reshape(xx.shape) ax.contourf(xx, yy, Z, cmap=cm, alpha=.8) # Plot also the training points ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright) # and testing points ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6) ax.set_xlim(xx.min(), xx.max()) ax.set_ylim(yy.min(), yy.max()) ax.set_xticks(()) ax.set_yticks(()) ax.set_title(name) ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), size=15, horizontalalignment='right') i += 1figure.subplots_adjust(left=.02, right=.98)plt.show()回答：
问题在于ds是一个包含超过两个值的列表，如下所示：
>>> ds=['rockatr1','rockatr2','rockatr','rocktype']>>> X,y=dsTraceback (most recent call last): File "<stdin>", line 1, in <module>ValueError: 要解包的值过多您必须指定哪部分是X，哪部分是y，如下所示。通常在分类数据中，最后一列用作标签，这就是我在这里所假设的。
>>> X,y=ds[:-1],ds[-1]>>> X['rockatr1', 'rockatr2', 'rockatr']>>> y'rocktype'