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# 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 Iris Flower Dataset

```
# Load data
iris = datasets.load_iris()
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.