C:\Users\chentj\PycharmProjects\pythonProject\venv\lib\site-packages\sklearn\base.py:443: UserWarning: X has feature names, but PCA was fitted without feature names warnings.warn( Traceback (most recent call last): File "C:\Users\chentj\PycharmProjects\pythonProject\云计算\课程设计\2.py", line 56, in <module> X_test_pca = pca.transform(X_test) # 使用训练集的 PCA 进行降维 File "C:\Users\chentj\PycharmProjects\pythonProject\venv\lib\site-packages\sklearn\decomposition\_base.py", line 120, in transform X = self._validate_data(X, dtype=[np.float64, np.float32], reset=False) File "C:\Users\chentj\PycharmProjects\pythonProject\venv\lib\site-packages\sklearn\base.py", line 600, in _validate_data self._check_n_features(X, reset=reset) File "C:\Users\chentj\PycharmProjects\pythonProject\venv\lib\site-packages\sklearn\base.py", line 400, in _check_n_features raise ValueError( ValueError: X has 3 features, but PCA is expecting 9 features as input. 代码出错原因

根据错误信息，可以看出代码出错的原因是PCA期望输入的特征数为9，但是实际输入的特征数为3。这可能是由于训练集和测试集的特征数不一致导致的。

您可以检查一下X_train和X_test的维度及特征数是否一致，如果不一致，可以对数据进行处理，使其具有相同的特征数。另外，您也可以检查一下PCA模型的参数设置是否正确，特别是n_components参数是否设置为期望的降维后的特征数。