我为岭回归创建了 python 代码。为此，我将交叉验证和网格搜索技术结合使用。我得到了输出结果。我想检查我的回归模型构建步骤是否正确？有人可以解释一下吗？

from sklearn.linear_model import Ridge
ridge_reg = Ridge()
from sklearn.model_selection import GridSearchCV
params_Ridge = {'alpha': [1,0.1,0.01,0.001,0.0001,0] , "fit_intercept": [True, False], "solver": ['svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga']}
Ridge_GS = GridSearchCV(ridge_reg, param_grid=params_Ridge, n_jobs=-1)
Ridge_GS.fit(x_train,y_train)
Ridge_GS.best_params_

输出 - {'alpha': 1, 'fit_intercept': True, 'solver': 'cholesky'}

Ridgeregression = Ridge(random_state=3, **Ridge_GS.best_params_)
from sklearn.model_selection import cross_val_score
all_accuracies = cross_val_score(estimator=Ridgeregression, X=x_train, y=y_train, cv=5)
all_accuracies

输出 - 数组（[0.93335508, 0.8984485, 0.91529146, 0.89309012, 0.90829416]）

print(all_accuracies.mean())

输出 - 0.909695864130532

Ridgeregression.fit(x_train,y_train)
Ridgeregression.score(x_test,y_test)

输出 - 0.9113458623386644

是 0.9113458623386644 我的岭回归精度（R sqred）吗？如果是，那么 0.909695864130532 值的含义是什么。