diff --git a/Other/Summer 2026 Projects.md b/Other/Summer 2026 Projects.md
index 73011cf..b29288f 100644
--- a/Other/Summer 2026 Projects.md	
+++ b/Other/Summer 2026 Projects.md	
@@ -8,4 +8,5 @@
 - FInish caldav cpp library
 - Workout routine
 	- Calisthenics
-	- *running*
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+	- *running*
+- Weather betting algorithm?
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diff --git a/Wiki/Machine Learning/Model Validation and Evaluation.md b/Wiki/Machine Learning/Model Validation and Evaluation.md
index 7fa4463..9b8772f 100644
--- a/Wiki/Machine Learning/Model Validation and Evaluation.md	
+++ b/Wiki/Machine Learning/Model Validation and Evaluation.md	
@@ -44,3 +44,7 @@ $\beta$ is used as a way to tune whether precision or recall is emphasized in th
 
 When $\beta = 1$ the F-measure is also called the F1-measure which is the most commonly used variant. This is where precision and recall are both weighted equally in the metric.
 ### Kappa
+$$\kappa = \frac{{p_{o} - p_{e}}}{1 - p_{e}}$$
+Where $p_{o}$ is the observed agreement and $p_{e}$ is the expected agreement. When using in the context of a binary classification problem $p_{o}$ is the observed accuracy of the model and $p_{e}$ is the expected number of times the model will agree with reality due to random chance (and the percentages that the model will pick each outcome)
+
+When using the metric in a binary classification problem with a 2x2 confusion matrix the formula can be written as $$\kappa = {2 * ()}$$
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