# Gaussian Naive Bayes Classifier

Because of the assumption of the normal distribution, Gaussian Naive Bayes is best used in cases when all our features are continuous.

## Preliminaries

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


# Load data
X = iris.data
y = iris.target


## Train Gaussian Naive Bayes Classifier

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

# Train model
model = clf.fit(X, y)


## Create Previously Unseen Observation

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


## Predict Class

# Predict class
model.predict(new_observation)

array([1])


Note: the raw predicted probabilities from Gaussian naive Bayes (outputted using predict_proba) are not calibrated. That is, they should not be believed. If we want to create useful predicted probabilities we will need to calibrate them using an isotonic regression or a related method.