Hyper-parameter tuning of NaiveBayes Classier I'm fairly new to machine learning and I'm aware of the concept of hyper-parameters tuning of classifiers, and I've come across a couple of examples of this technique. However, I'm trying to use NaiveBayes Classifier of sklearn for a task but I'm not sure about the values of the parameters that I should try.

What I want is something like this, but for GaussianNB() classifier and not SVM:

from sklearn.model_selection import GridSearchCV
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']
hyper={'kernel':kernel,'C':C,'gamma':gamma}
gd=GridSearchCV(estimator=svm.SVC(),param_grid=hyper,verbose=True)
gd.fit(X,Y)
print(gd.best_score_)
print(gd.best_estimator_)


I've tried to search for examples for NaiveBayes, but couldn't find any. What I have right now is simply this:

model = GaussianNB()


What I want is to try different parameters and compare the scores.
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