机器学习入门-使用GridSearch进行网格参数搜索GridSeach(RandomRegressor(), param_grid, cv=3)

from sklearn.grid_search import GridSearchCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets.california_housing import fetch_california_housing

# 载入数据
housing = fetch_california_housing()
# 列出参数列表
tree_grid_parameter = {‘min_samples_split‘:list((3, 6, 9)), ‘n_estimators‘:list((10, 50, 100))}
# 进行参数的搜索组合
grid = GridSearchCV(RandomForestRegressor(), param_grid=tree_grid_parameter, cv=3)
grid.fit(train_x, train_y)
print(grid.grid_scores_) # 打印得分
print(grid.best_params_) # 打印最好的参数组合
print(grid.best_score_)  # 打印最好的得分