Building a classifier based on Gaussian Mixture Models

Let's build a classifier based on a Gaussian Mixture Model. Create a new Python file and import the following packages:

import numpy as np 
import matplotlib.pyplot as plt 
from matplotlib import patches 
 
from sklearn import datasets 
from sklearn.mixture import GMM 
from sklearn.cross_validation import StratifiedKFold 

Let's use the iris dataset available in scikit-learn for analysis:

# Load the iris dataset 
iris = datasets.load_iris() 

Split the dataset into training and testing using an 80/20 split. The n_folds parameter specifies the number of subsets you'll obtain. We are using a value of 5, which means the dataset will be split into five parts. We will use four parts for training and one part for testing, which gives a split of 80/20:

# Split dataset into training and testing (80/20 split) 
indices = StratifiedKFold(iris.target, n_folds=5) 

Extract the training data:

# Take...