模型参数_grid

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

from sklearn.model_selection import GridSearchCV

from sklearn.svm import SVR

iris = datasets.load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)



sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)
# 网格搜索优化调参# #设置参数
parameters = {'C':[1,10,20,40,60,80],'gamma':[0.2,0.4,0.6,0.8]}

#查询最优参数

grid = GridSearchCV(estimator=SVR(),param_grid=parameters)
grid.fit(X_train_std,y_train)
#搜索结果
print('最高得分：%.3f' %grid.best_score_)
print('最优参数：%s %s'%(grid.best_estimator_.C,grid.best_estimator_.gamma))#最优参数：0.8 10