现在，如果我尝试预测：

themodel.predict(X_val)，我会收到：

ValueError: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 6 is different from 2)

我必须这样做：

pol_feat = PolynomialFeatures(degree=2)themodel.predict(pol_feat.fit_transform(X_val))

才能正常工作。所以，如何存储这个信息，以便能够使用模型进行预测？

回答：

你还需要pickle训练好的PolynomialFeatures：

# train and picklepoly_reg = PolynomialFeatures(degree = 2)X_poly = poly_reg.fit_transform(X_train)poly_reg_model = LinearRegression().fit(X_poly, y_train)joblib.dump(poly_reg_model, 'themodel')joblib.dump(poly_reg, 'poilynomia_features_model')# load and predictpoilynomia_features_model = joblib.load('poilynomia_features_model')themodel = joblib.load('themodel')X_val_prep = poilynomia_features_model.transform(X_val)predictions = themodel.predict(X_val_prep)

但更好的做法是将所有步骤包装在一个单一的pipeline中：

pipeline = Pipeline(steps=[('poilynomia', PolynomialFeatures()),                            ('lr', LinearRegression())])pipeline.fit(X_train, y_train)pipeline.predict(X_val)