How to specify the prior probability for scikit-learn's Naive Bayes

Question

I\'m using the scikit-learn machine learning library (Python) for a machine learning project. One of the algorithms I\'m using is the Gaussian Naive Bayes implementation. On

Answer 1

The GaussianNB() implemented in scikit-learn does not allow you to set class prior. If you read the online documentation, you see .class_prior_ is an attribute rather than parameters. Once you fit the GaussianNB(), you can get access to class_prior_ attribute. It is calculated by simply counting the number of different labels in your training sample.

from sklearn.datasets import make_classification
from sklearn.naive_bayes import GaussianNB


# simulate data with unbalanced weights
X, y = make_classification(n_samples=1000, weights=[0.1, 0.9])
# your GNB estimator
gnb = GaussianNB()
gnb.fit(X, y)

gnb.class_prior_
Out[168]: array([ 0.105,  0.895])

gnb.get_params()
Out[169]: {}

You see the estimator is smart enough to take into account the unbalanced weight issue. So you don't have to manually specify the priors.