我在Kaggle上学习机器学习教程时，按照教程逐行操作，但仍然遇到了ValueError。我正在尝试练习数据验证和拆分。这是我的代码：

import pandas as pdfrom sklearn.tree import DecisionTreeRegressorfrom sklearn.model_selection import train_test_splitmain_file_path = '../input/train.csv'data = pd.read_csv(main_file_path)y = data.SalePricedata_predictors = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']x = data[data_predictors]train_x, val_x, train_y, val_x = train_test_split(x, y,random_state = 0)data_model = DecisionTreeRegressor()data_model.fit(train_x,train_y)data_prediction = data_model.predict(val_x)print(mean_absolute_error(val_y, data_prediction))

错误指向这一行：

data_prediction = data_model.predict(val_x)

我是一个机器学习的初学者，所以我将我的代码与作者的代码进行了比较，发现实现是相同的。

完整的堆栈跟踪：

---------------------------------------------------------------------------ValueError                                Traceback (most recent call last)<ipython-input-3-48f37072f996> in <module>()     17 data_model.fit(train_x,train_y)     18 ---> 19 data_prediction = data_model.predict(val_x)     20 print(mean_absolute_error(val_y, data_prediction))/opt/conda/lib/python3.6/site-packages/sklearn/tree/tree.py in predict(self, X, check_input)    410         """    411         check_is_fitted(self, 'tree_')--> 412         X = self._validate_X_predict(X, check_input)    413         proba = self.tree_.predict(X)    414         n_samples = X.shape[0]/opt/conda/lib/python3.6/site-packages/sklearn/tree/tree.py in _validate_X_predict(self, X, check_input)    371         """Validate X whenever one tries to predict, apply, predict_proba"""    372         if check_input:--> 373             X = check_array(X, dtype=DTYPE, accept_sparse="csr")    374             if issparse(X) and (X.indices.dtype != np.intc or    375                                 X.indptr.dtype != np.intc):/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)    439                     "Reshape your data either using array.reshape(-1, 1) if "    440                     "your data has a single feature or array.reshape(1, -1) "--> 441                     "if it contains a single sample.".format(array))    442             array = np.atleast_2d(array)    443             # To ensure that array flags are maintainedValueError: Expected 2D array, got 1D array instead:

回答：

虽然错误出现在你指出的那行，但实际问题出在这行：

    train_x, val_x, train_y, val_x = train_test_split(x, y,random_state = 0)

请注意，你在这行代码中有两个val_x。第二个val_x应该是val_y。发生的情况是，你将val_x（应该是一个二维的输入数组）设置成了本应是y值的一维预测数组，从而导致ValueError，提示你输入了一维数组而期望的是二维数组。