scikit-learn的栅格搜索最优参数

import numpy as np
from sklearn.linear_model import Ridge
from sklearn.grid_search import GridSearchCV

prepare a range of alpha values to test

 alphas = np.array([1,0.1,0.01,0.001,0.0001,0])

create and fit a ridge regression model

model = Ridge()
grid = GridSearchCV(estimator=model, param_grid=dict(alpha=alphas))
grid.fit(X, y)
print(grid)

summarize the results of the grid search

print(grid.best_score_)
print(grid.best_estimator_.alpha)