我在数据框上使用多项式回归。这是代码

from sklearn.preprocessing import PolynomialFeaturesfrom sklearn import metricsfrom sklearn.metrics import mean_squared_errorfrom math import sqrtfrom sklearn.linear_model import LinearRegressionpoly_reg = PolynomialFeatures(3)lin_reg_2 = LinearRegression()iv_poly = poly_reg.fit_transform(X_train)poly_reg.fit(iv_poly, y_train)lin_reg_2.fit(iv_poly, y_train)predictValues = lin_reg_2.predict(X_test)print("RMSE is:-") print(np.sqrt(metrics.mean_squared_error(y_test, predictValues)))

错误：

ValueError                                Traceback (most recent call last)<ipython-input-4-a77f27b571a6> in <module>()    106     107 --> 108 predictValues = lin_reg_2.predict(X_test)    109 print("RMSE is:-")    110 print(np.sqrt(metrics.mean_squared_error(y_test, predictValues)))~\Anaconda3\lib\site-packages\sklearn\linear_model\base.py in predict(self, X)    254             Returns predicted values.    255         """--> 256         return self._decision_function(X)    257     258     _preprocess_data = staticmethod(_preprocess_data)~\Anaconda3\lib\site-packages\sklearn\linear_model\base.py in _decision_function(self, X)    239         X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])    240         return safe_sparse_dot(X, self.coef_.T,--> 241                                dense_output=True) + self.intercept_    242     243     def predict(self, X):~\Anaconda3\lib\site-packages\sklearn\utils\extmath.py in safe_sparse_dot(a, b, dense_output)    138         return ret    139     else:--> 140         return np.dot(a, b)    141     142 ValueError: shapes (18,17) and (1140,1) not aligned: 17 (dim 1) != 1140 (dim 0)

我使用了线性回归，没有出现任何错误，并且成功计算了RMSE，但是应用多项式回归时，它出现了错误

回答：

你没有转换 X_test

iv_poly_test = poly_reg.transform(X_test)lin_reg_2.predict(iv_poly_test)