Python GridSearchCV和ValueError:估计器管道的参数alpha无效

我想将

StandardScaler

与

GridSearchCV

一起使用，并为

Ridge

回归模型找到最佳参数

但我得到了以下错误：

raise VALUERROR（'参数%s对于估计器%s无效' ValueError:估计器管道的参数alpha无效（内存=无， 步骤=[（'standardscaler'，standardscaler（copy=True，with_mean=True，with_std=True）），（'ridge'，ridge（alpha=1.0，copy=True，fit=True，max=None，normalize=False，random=None， solver='auto'，tol=0.001）），verbose=False）。使用估计器检查可用参数列表。get_params（）.keys（）

有人能帮我吗

import  numpy   as   np; import  pandas  as   pd; import  matplotlib.pyplot  as  plt;
import  plotly.express   as  px
from sklearn.linear_model import LinearRegression, Ridge,Lasso, ElasticNet
from sklearn.model_selection import cross_val_score,GridSearchCV, train_test_split
from sklearn.metrics import mean_squared_error
x_data=pd.read_excel('Input-15.xlsx')
y_data=pd.read_excel('Output-15.xlsx')
X_train, X_test,Y_train,Y_test=train_test_split(x_data,y_data,test_size=0.2,random_state=42)
###########    Ridge regression model     ########### 
rige=Ridge(normalize=True)
rige.fit(X_train,Y_train["Acc"]);rige.score(X_test,Y_test["Acc"])
score=format(rige.score(X_test,Y_test["Acc"]),'.4f')
print ('Ridge Reg Score with Normalization:',score)
from sklearn.pipeline import make_pipeline, Pipeline
from sklearn.preprocessing import StandardScaler
pip=make_pipeline(StandardScaler(),Ridge())
pip.fit(X_train,Y_train["Acc"])
score_pipe=format(pip.score(X_test,Y_test["Acc"]),'.4f')
print ('Standardized Ridge Score:',score_pipe)
######  performing the GridSearchCV /the value of α that maximizes the R2 ####
param_grid = {'alpha': np.logspace(-3,3,10)}
grid = GridSearchCV(estimator=pip, param_grid=param_grid, cv=2,return_train_score=True)
grid.fit(X_train,Y_train["Acc"])### barayeh har khoroji  ********
best_score = float(format(grid.best_score_, '.4f'))
print('Best CV score: {:.4f}'.format(grid.best_score_))
print('Best parameter :',grid.best_params_)

---

简短的回答是：改变这一行：

param_grid = {'alpha': np.logspace(-3,3,10)}

致：

通常，所有可在

GridSearchCV

中调谐的参数都可以通过

estimator.get_params（）.keys（）

就你的情况而言：

pip.get_params().keys()

dict_keys(['memory', 'steps', 'verbose', 'standardscaler', 'ridge',
 'standardscaler__copy', 'standardscaler__with_mean',
 'standardscaler__with_std', 'ridge__alpha', 'ridge__copy_X',
 'ridge__fit_intercept', 'ridge__max_iter', 'ridge__normalize',
 'ridge__random_state', 'ridge__solver', 'ridge__tol'])

另一方面，为什么不使用分号在新行上启动所有新语句？相关代码的空格块？这将使代码更具可读性

pip.get_params().keys()

dict_keys(['memory', 'steps', 'verbose', 'standardscaler', 'ridge',
 'standardscaler__copy', 'standardscaler__with_mean',
 'standardscaler__with_std', 'ridge__alpha', 'ridge__copy_X',
 'ridge__fit_intercept', 'ridge__max_iter', 'ridge__normalize',
 'ridge__random_state', 'ridge__solver', 'ridge__tol'])