from pandas import read_csv
from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV
# 导入数据
filename = 'pima_data.csv'
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
data = read_csv(filename, names=names)
# 将数据分为输入数据和输出结果
array = data.values
X = array[:, 0:8]
Y = array[:, 8]
# 算法实例化
model = Ridge()
# 设置要遍历的参数
param_grid = {'alpha': [1, 0.1, 0.01, 0.001, 0]}
# 通过网格搜索查询最优参数
grid = GridSearchCV(estimator=model, param_grid=param_grid)
grid.fit(X, Y)
# 搜索结果
print('最高得分：%.3f' % grid.best_score_)
print('最优参数：%s' % grid.best_estimator_.alpha)
最高得分：0.280
最优参数：1

from pandas import read_csv
from sklearn.linear_model import Ridge
from sklearn.model_selection import RandomizedSearchCV
from scipy.stats import uniform
# 导入数据
filename = 'pima_data.csv'
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
data = read_csv(filename, names=names)
# 将数据分为输入数据和输出结果
array = data.values
X = array[:, 0:8]
Y = array[:, 8]
# 算法实例化
model = Ridge()
# 设置要遍历的参数
param_grid = {'alpha': uniform()}
# 通过网格搜索查询最优参数
grid = RandomizedSearchCV(estimator=model, param_distributions=param_grid, n_iter=100, random_state=7)
grid.fit(X, Y)
# 搜索结果
print('最高得分：%.3f' % grid.best_score_)
print('最优参数：%s' % grid.best_estimator_.alpha)
D:\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.
  warnings.warn(CV_WARNING, FutureWarning)


最高得分：0.280
最优参数：0.9779895119966027