Measuring the performance of a clustering algorithm is a little trickier than classification or regression, because clustering is unsupervised machine learning. Thankfully, scikit-learn comes equipped to help us with this as well in a very straightforward manner.
Clustering metrics
Getting ready
To measure clustering performance, start by loading the iris dataset. We will relabel the iris flowers as two types: type 0 is whenever the target is 0 and type 1 is when the target is 1 or 2:
from sklearn.datasets import load_iris
import numpy as np
iris = load_iris()
X = iris.data
y = np.where(iris.target == 0,0,1)
