### What are some common evaluation metrics in clustering?

Since there are no labels associated with the observations in unsupervised learning, there is no direct error metric that can be applied

- Machine Learning 101 (30)
- Statistics 101 (38)
- Supervised Learning (113)
- Unsupervised Learning (55)
- Deep Learning (23)
- Data Preparation (36)
- General (6)
- Standardization (6)
- Missing data (7)
- Textual Data (17)

Since there are no labels associated with the observations in unsupervised learning, there is no direct error metric that can be applied

The WCSS is a measure of the variability of observations within clusters.

Read more..

The Rand Index can be used for comparing the results from multiple clustering algorithms.

The ARI adjusts the raw Rand Index for classification by chance by subtracting the expected index from both the numerator and denominator.

Silhouette Score compares the distance of observations to the centroids of the clusters they are assigned to against that to the centroids of other clusters in an algorithm like K-Means.

The Dunn Index is a ratio of the smallest distance between observations assigned to different clusters over the largest distance between observations assigned to the same cluster.

**Partner Ad**

Find out all the ways

that you can

- Machine Learning 101 (30)
- Statistics 101 (38)
- Supervised Learning (113)
- Unsupervised Learning (55)
- Deep Learning (23)
- Data Preparation (36)
- General (6)
- Standardization (6)
- Missing data (7)
- Textual Data (17)