vault backup: 2026-05-21 18:33:15

.obsidian/graph.json

@@ -17,6 +17,6 @@
   "repelStrength": 10,
   "linkStrength": 1,
   "linkDistance": 250,
-  "scale": 0.2022585800147419,
+  "scale": 0.1719770322750558,
   "close": true
 }

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

@@ -5,4 +5,12 @@
 	- sampling is random
 	- Out of bag sample
 		- data that was never picked in bootstrapping
-		- 
+		- is often used for testing data (OOB samples were not in the training samples so the model has never seen them)
+		- with common techniques often ~30% of data is not picked (and forms OOB set)
+	- distribution of classes with bootstrapping will form a Gaussian distribution
+	- when enough samples are used bootstrap distributions will approximate population statistics
+		- Assumption is made with bootstrapping that the sample approximates the original population
+	- bootstrap samples are used as training sets, OOB samples serve as testing sets
+```python
+bootstrap_samples = [resample(df, replace=True, random_state=i) for i in range(5)]
+```