Ridge

# 기본 모델 적용
from sklearn.linear_model import Ridge
model = Ridge()

# Grid Search
param_grid = {'alpha' : [1e-4, 1e-3, 1e-2, 0.1, 0.5, 1.0, 5.0, 10.0]}
from sklearn.model_selection import GridSearchCV
gs = GridSearchCV(Ridge(), param_grid, cv= 5)

# Ramdom Search
from scipy.stats import randint
param_distribs = {'alpha' : randint(low = 0.001, high = 100)}
from sklearn.model_selection import RandomizedSearchCV
rs = RandomizedSearchCV(Ridge(), param_distributions = param_distribs, cv= 5, n_iter = 100)

Lasso

# 기본 모델 적용
from sklearn.linear_model import Lasso
model = Lasso()

# Grid Search
param_grid={'alpha': [0.0, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 0.1, 0.5, 1.0, 2.0, 3.0]}
from sklearn.model_selection import GridSearchCV
grid_search=GridSearchCV(Lasso(), param_grid, cv=5)

# Ramdom Search
from scipy.stats import randint
param_distribs = {'alpha': randint(low=0.00001, high=10)}
from sklearn.model_selection import RandomizedSearchCV
random_search=RandomizedSearchCV(Lasso(), 
                                 param_distributions=param_distribs, n_iter=100, cv=5)

ElasticNet

# 기본 모델 적용
from sklearn.linear_model import ElasticNet
model=ElasticNet()

# Grid Search
param_grid={'alpha': [0.0, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 0.1, 0.5, 1.0, 2.0, 3.0]}
from sklearn.model_selection import GridSearchCV
grid_search=GridSearchCV(ElasticNet(), param_grid, cv=5)


# Ramdom Search
from scipy.stats import randint
param_distribs = {'alpha': randint(low=0.00001, high=10)}
from sklearn.model_selection import RandomizedSearchCV
random_search=RandomizedSearchCV(ElasticNet(), 
                                 param_distributions=param_distribs, n_iter=100, cv=5)