# Load libraries
from sklearn import datasets
from sklearn.naive_bayes import GaussianNB

# Load data
iris = datasets.load_iris()
features = iris.data
target = iris.target

# Create Gaussian Naive Bayes object
classifer = GaussianNB()

# Train model
model = classifer.fit(features, target)

# Create new observation
new_observation = [[ 4,  4,  4,  0.4]]

# Predict class
model.predict(new_observation)

# Create Gaussian Naive Bayes object with prior probabilities of each class
clf = GaussianNB(priors=[0.25, 0.25, 0.5])

# Train model
model = classifer.fit(features, target)
