{"id":5648976,"date":"2023-09-22T10:16:47","date_gmt":"2023-09-22T14:16:47","guid":{"rendered":"https:\/\/lightning.ai\/pages\/?p=5648976"},"modified":"2023-09-27T06:14:57","modified_gmt":"2023-09-27T10:14:57","slug":"torchmetrics-v1.2","status":"publish","type":"post","link":"https:\/\/lightning.ai\/pages\/community\/announcements\/torchmetrics-v1.2\/","title":{"rendered":"Torchmetrics v1.2"},"content":{"rendered":"

Torchmetrics v1.2 is out now! The latest release includes 11 new metrics within a new subdomain: Clustering<\/em>. In this blog post, we briefly explain what clustering is, why it\u2019s a useful measure, and newly added metrics that can be used with code samples.<\/p>\n

Clustering: What is it?<\/h2>\n

Clustering is an unsupervised learning<\/em> technique. The term unsupervised here refers to the fact that we do not have ground truth targets as we do in classification. The primary goal of clustering is to discover hidden patterns or structures within data without having any prior knowledge about the data meaning or importance of particular features.<\/strong> Thus, clustering is a form of data exploration compared to supervised<\/em> learning, where the goal is \u201cjust\u201d to predict if a data point belongs to one class.<\/p>\n

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Clustering is, at a glance, closely related to classification; however, there are core differences. In classification, we know beforehand how many classes we are trying to model. For clustering, we do not. Figure from here<\/a>.<\/p><\/div>\n

The key goal of clustering algorithms<\/em> is to split data into clusters\/sets where data points from the same cluster are more similar to each other than any other points from the remaining clusters. Some of the most common and widely used clustering algorithms are K-Means<\/a>, Hierarchical clustering<\/a>, and Gaussian Mixture Models (GMM)<\/a>.<\/p>\n

An objective quality evaluation\/measure is required regardless of what clustering algorithm or internal optimization criterion is used. In general, we can divide all clustering metrics into two categories: extrinsic metrics and intrinsic metrics.<\/p>\n

Extrinsic metrics<\/h3>\n

Extrinsic metrics are characterized by requirements of some kind of ground truth labeling, even if it is used for an unsupervised method. This may seem counter-intuitive at first as we, by clustering definition, do not use such ground truth labeling. However, most clustering algorithms are still developed on datasets that have labels available so these metrics use this fact as an advantage.<\/p>\n

Takeaways<\/h3>\ud83d\udca1 NOTE: The core difference between extrinsic clustering metrics and classification metrics is that clustering metrics still allow for a different number of clusters in the ground truth labeling vs. the predicted labeling. <\/div>\n

In total, 8 of the 11 new clustering metrics fall into this category:<\/p>\n