We now train the LogisticRegression model. If you've chosen to do the gridsearch, you can plug the value of C into the model declaration (e.g. `LogisticRegression(C=5.2)`). code example

Example: Exhaustive search over specified parameter values for an estimator

# Exhaustive search over specified parameter values for an estimator

from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
iris = datasets.load_iris()
parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
svc = svm.SVC()
clf = GridSearchCV(svc, parameters)
clf.fit(iris.data, iris.target)
# GridSearchCV(estimator=SVC(),
#              param_grid={'C': [1, 10], 'kernel': ('linear', 'rbf)})
sorted(clf.cv_results_.keys())
# ['mean_fit_time', 'mean_score_time', 'mean_test_score',...
#  'param_C', 'param_kernel', 'params',...
#  'rank_test_score', 'split0_test_score',...
#  'split2_test_score', ...
#  'std_fit_time', 'std_score_time', 'std_test_score']