Hyper-parameter tuning of NaiveBayes Classier
from sklearn.model_selection import GridSearchCV

hyper = {'C':[0.05,0.1,0.2,0.3,0.25,0.4,0.5,0.6,0.7,0.8,0.9,1],
         'gamma':[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0],
         'kernel':['rbf','linear']
        }

gd=GridSearchCV(estimator=svm.SVC(),param_grid=hyper,verbose=True)

gd.fit(X,Y)
print(gd.best_score_)
print(gd.best_estimator_)

Sources:

Hyperparameter Tuning of Machine Learning Model in Python

https://github.com/dataprofessor/code/blob/master/python/hyperparameter_tuning.ipynb
https://www.youtube.com/watch?v=AvWfL1Us3Kg