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

@@ -77,4 +77,8 @@ $$MSE = \frac{1}{n}\sum(\hat{Y}_{i} - Y_{i})^2$$
 Since MSE produces a convex curve in the error metric it also allows the use of algorithms like gradient descent optimization to tune the weights of a model.
 ### Log Loss
 $$L_{\log}(y_{i}, \hat{p}_{i}) = -(y_{i}\ln(\hat{p}_{i}) + (1-y_{i})\ln(1-\hat{p}_{i}))$$
-Log loss is used to optimize the weights of a logistic regression. MSE cannot be used since a logistic regression is not linear and the error with respect to weights does not have a clear minimum. This makes it challengin
+Log loss is used to optimize the weights of a logistic regression. MSE cannot be used since a logistic regression is not linear and the error with respect to weights is not a convex curve. This makes it challenging to find the weights since algorithms like gradient descent cannot be used.
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+Logistic regressions also only predict between 0 - 1 (probabilities) so any error value will be between 0 - 1 using MSE which is not ideal.
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+The log loss function is used to penalize wrong predictions more harshly when they are more confident.