Internal evaluation metrics are really important in unsupervised learning.
They help us understand how well our models are working, even when we don't have labeled data.
In simple terms, they show us how good our clusters or groups are.
Here are a couple of popular metrics:
Silhouette Score: This score goes from -1 to 1. A value close to 1 means that the points are nicely grouped together. I think it's really helpful for seeing how connected each data point is within its cluster.
Davies-Bouldin Index: Lower values are better when it comes to clustering. This metric looks at how close points are in the same cluster versus how far apart they are from points in other clusters. It’s a cool way to see how well our clusters stand out from each other.
From my experience, using these metrics is great. They guide us in fine-tuning the model and make sure that what we get in the end makes sense and is easy to understand.
Internal evaluation metrics are really important in unsupervised learning.
They help us understand how well our models are working, even when we don't have labeled data.
In simple terms, they show us how good our clusters or groups are.
Here are a couple of popular metrics:
Silhouette Score: This score goes from -1 to 1. A value close to 1 means that the points are nicely grouped together. I think it's really helpful for seeing how connected each data point is within its cluster.
Davies-Bouldin Index: Lower values are better when it comes to clustering. This metric looks at how close points are in the same cluster versus how far apart they are from points in other clusters. It’s a cool way to see how well our clusters stand out from each other.
From my experience, using these metrics is great. They guide us in fine-tuning the model and make sure that what we get in the end makes sense and is easy to understand.