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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
+$$CE = -\sum{Y_{i} * \log(\hat{Y}_{i})}$$