vault backup: 2026-05-28 19:05:44

Excalidraw/Drawing 2026-05-28 19.05.10.excalidraw.md

@@ -0,0 +1,14 @@
+---
+
+excalidraw-plugin: parsed
+tags: [excalidraw]
+
+---
+==⚠  Switch to EXCALIDRAW VIEW in the MORE OPTIONS menu of this document. ⚠== You can decompress Drawing data with the command palette: 'Decompress current Excalidraw file'. For more info check in plugin settings under 'Saving'
+
+
+## Drawing
+```compressed-json
+N4IgLgngDgpiBcIYA8DGBDANgSwCYCd0B3EAGhADcZ8BnbAewDsEAmcm+gV31TkQAswYKDXgB6MQHNsYfpwBGAOlT0AtmIBeNCtlQbs6RmPry6uA4wC0KDDgLFLUTJ2lH8MTDHQ0YNMWHRJFkUWAGZFULIkT1UYRjAaBABtAF1ydCgoAGUAsD5QWRhYhBBcdHwAayjJfDws7A0+Rk5MTHIdGCIAIXRUCpquRlwAYXpMenwSgGIAMzn5kABfRaA==
+```
+%%

Running Start/CSB320 - Machine Learning Concepts/Class 5-28 (Clustering).md

@@ -32,4 +32,7 @@
 			- elbow method
 				- graph inertia against k and find point where elbow of data is (curve levels off)
 			- Silhouette method
-				- Use silhouette coeffic
+				- Use silhouette coefficient to determine separation of clusters $$S(j) = \frac{{\overline{d_{out}(j)} - \overline{d_{in}(j)}}}{max(\overline{d_{out}(j)}, \overline{d_{in}(j)})}$$
+				- $\overline{d_{out}(j)}$ is the average distance of the instance $i$ to the centroid of all other clusters
+				- $\overline{d_{in}(j)}$ is the average distance of the instance $i$ to all other instances in its cluster
+				-