The Naive Bayes model using Bayes therem for conditional probability to classify observations. This helps solve many types of problems with data sets and also gives us back easy to interpret probabilities for different classes. In this article, we will learn how to train a Niave Bayes classifier with Sklearn.
To create a naive bayes algorithm, we use the
GaussianNB class from the
naive_bayes module. We create an instance of
GaussianNB then use the
fit method on our features and target to get the final model.
from sklearn import datasets from sklearn.naive_bayes import GaussianNB iris = datasets.load_iris() features = iris.data target = iris.target gauss = GaussianNB() model = gauss.fit(features, target) print(model.score())