vault backup: 2026-05-21 19:03:38

Running Start/CSB320 - Machine Learning Concepts/Class 5-21 (Ensemble Models).md

@@ -32,4 +32,8 @@ bootstrap_samples = [
 	- bagging reduces model variance, not data variance
 		- (how much does model change if data changes slightly)
 	- bagging helps to make predictions more stable
+	- model variance should be low to increase prediction stability, data variance should be high so patterns are easier to find
+	- 10-25 decision trees are typically sufficient
+		- too many results in diminishing returns
+	- bagging gives high accuracy and better generalization but can be hard to interpret and have large computational costs
 	-