vault backup: 2026-05-21 18:43:20

.obsidian/graph.json

@@ -17,6 +17,6 @@
   "repelStrength": 10,
   "linkStrength": 1,
   "linkDistance": 250,
-  "scale": 0.1719770322750558,
+  "scale": 0.18153036664097952,
   "close": true
 }

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

@@ -12,5 +12,23 @@
 		- 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)]
+bootstrap_samples = [
+	resample(df, replace=True, n_samples=n random_state=i) for i in range(5)
+]
 ```
+- gets a list of dataframes with sampled data
+- ensemble models can often greatly improve performance
+	- decision trees are often used as bases
+	- can increase computational complexity
+- parallel ensembles
+	- base models are trained independently
+	- training is faster
+- sequential ensembles
+	- models trained iteratively, adjusting for previous errors
+- bagging
+	- (bootstrap aggregation)
+	- create bootstrap sets, train weak models and then aggregate predictions to get more accurate prediction
+	- decision trees in bagging have low bias but high variance
+	- bagging reduces model variance, not data variance
+		- (how much does model change if data changes slightly)
+		-