What is the homogeneity score in machine learning? The homogeneity score is a metric using which we can measure clustering performance in machine learning. A clustering result is said to be homogeneous if it contains data points that are members of a single class. A...

What is the completeness score in clustering? The completeness score of clustering indicates whether all the data points that are members of a given class belong to the same cluster. It is a floating point number between 0 and 1. A completeness score of 1 indicates...

What is the silhouette score? The silhouette coefficient of a sample is calculated as follows: $latex \frac{b-a}{max(a,b)} $ Here, a is the distance between the sample and the centroid of the cluster to which the sample belongs. And b is the distance between the...

We have already discussed what k-means clustering is and how it works. We also discussed how to perform k-means clustering using the sklearn Python library. We saw that k-means clustering is a simple algorithm that can be applied to large datasets. But, the...

K-means clustering is an unsupervised learning algorithm that can be used for solving clustering problems in machine learning. K-means clustering takes a bunch of unlabeled data and groups them into k clusters. The clustering is done so that each point belongs to its...