CODEWITHSUNDEEP
pythonmachine-learningscikit-learnnaivebayes
vlad
vlad

Reputation: 811

How to tune GaussianNB?

Trying to fit data with GaussianNB() gives me low accuracy score.

I'd like to try Grid Search, but it seems that parameters sigma and theta cannot be set. Is there anyway to tune GausssianNB?

Upvotes: 20

Views: 33999

Answers (4)

Pavel Fedotov
Pavel Fedotov

Reputation: 885

In an sklearn pipeline it may look as follows:

pipe = Pipeline(steps=[
                    ('pca', PCA()),
                    ('estimator', GaussianNB()),
                    ])
    
parameters = {'estimator__var_smoothing': [1e-11, 1e-10, 1e-9]}
Bayes = GridSearchCV(pipe, parameters, scoring='accuracy', cv=10).fit(X_train, y_train)
print(Bayes.best_estimator_)
print('best score:')
print(Bayes.best_score_)
predictions = Bayes.best_estimator_.predict(X_test)

Upvotes: 1

Helen Batson
Helen Batson

Reputation: 341

As of version 0.20

GaussianNB().get_params().keys() returns 'priors' and 'var_smoothing'

A grid search would look like:

pipeline = Pipeline([
    ('clf', GaussianNB())
])

parameters = {
    'clf__priors': [None],
    'clf__var_smoothing': [0.00000001, 0.000000001, 0.00000001]
}

cv = GridSearchCV(pipeline, param_grid=parameters)

cv.fit(X_train, y_train)
y_pred_gnb = cv.predict(X_test)

Upvotes: 4

ana
ana

Reputation: 368

You can tune 'var_smoothing' parameter like this:

nb_classifier = GaussianNB()

params_NB = {'var_smoothing': np.logspace(0,-9, num=100)}
gs_NB = GridSearchCV(estimator=nb_classifier, 
                 param_grid=params_NB, 
                 cv=cv_method,   # use any cross validation technique 
                 verbose=1, 
                 scoring='accuracy') 
gs_NB.fit(x_train, y_train)

gs_NB.best_params_

Upvotes: 16

Matheus Schaly
Matheus Schaly

Reputation: 303

Naive Bayes doesn't have any hyperparameters to tune.

Upvotes: -1

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