vault backup: 2026-05-21 16:04:53

.obsidian/graph.json

@@ -17,6 +17,6 @@
   "repelStrength": 10,
   "linkStrength": 1,
   "linkDistance": 250,
-  "scale": 0.20021871622586232,
+  "scale": 0.2022585800147419,
   "close": true
 }

Excalidraw/Drawing 2026-05-21 10.46.38.excalidraw.md

@@ -1,14 +0,0 @@
----
-
-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+gV31TkQAswYKDXgB6MQHNsYfpwBGAOlT0AtmIBeNCtlQbs6RmPry6uA4wC0KDDgLFLUTJ2lH8MTDHQ0YNMWHRJFkUWAGZFULIkT1UYRjAaBABtAF1ydCgoAGUAsD5QSXw8LOwNPkZOTExyHRgiACF0VABrQq5GXABhekx6fAQQAGIAM1GxkABfCaA==
-```
-%%

Running Start/CSB320 - Machine Learning Concepts/Class 4-23 (Model Validation & Evaluation).md

@@ -66,7 +66,7 @@ There are many ways to evaluate the performance of models. This is necessary to
 ## Loss Functions
 Loss functions are meant to quantify the difference between observed values and the predictions of a model. They can be used to minimize loss and improve model performance.
 ### Absolute Loss
-$$Y_{i} - \hat{Y}_{i}$$
+$$|Y_{i} - \hat{Y}_{i}|$$
 Used to measure the difference from the observed value and predicted value by the model.
 ### Mean Absolute Error
 $$MAE=\frac{1}{n}\sum|\hat{Y}_{i} - Y_{i}|$$
@@ -84,4 +84,6 @@ Logistic regressions also only predict between 0 - 1 (probabilities) so any erro
 The log loss function is used to penalize wrong predictions more harshly when they are more confident.
 
 ![[LogLoss.excalidraw]]
-Only one of the terms inside of the parentheses will be non-zero based on if the observed class is 0 or 1. The log loss function then penalizes the incorrect prediction much more heavily the closer it is to the incorrect value
+Only one of the terms inside of the parentheses will be non-zero based on if the observed class is 0 or 1. The log loss function then penalizes the incorrect prediction much more heavily the closer it is to the incorrect value.
+### Cross Entropy Loss
+$$CE = -\sum{Y_{i} * \log(\hat{Y}_{i})}$$