vault backup: 2026-06-19 21:07:17

Wiki/Linux/Full Fedora Install Instructions.md

@@ -169,6 +169,11 @@ generated config needs to be moved
 sudo dnf copr enable erikreider/SwayNotificationCenter
 sudo dnf install SwayNotificationCenter
 ```
+#### Tuxedo
+Go to [https://github.com/webstonehq/tuxedo](https://github.com/webstonehq/tuxedo) and download the latest release, move it to
+```
+/usr/bin
+```
 ### Adding Configs
 ```bash
 cd ~

Wiki/Linux/Syncing todo.txt Across Devices.md

@@ -0,0 +1,5 @@
+#linux 
+- - -
+To manage my to-do list I have been using my self-hosted Radicale server and the app [planify](https://useplanify.com/). The Radicale server is also where I store my calendars and is a great way to sync them between my devices. Using iCal allowed me to use planify and gnome-calendar on my Linux laptop and the iPhone calendar and reminders app for mobile. I came across a todo app called [tuxedo](https://github.com/webstonehq/tuxedo) though and I wanted to give it a try since the interface seemed really nice.
+
+Tuxedo uses the todo.txt format which is a plain-text format designed to be readable by machines and humans. I like the project and context sorting this format gives and the term

Wiki/Machine Learning/Imbalanced Data.md

@@ -19,4 +19,6 @@ The synthetic minority oversampling technique (SMOTE) is used to generate synthe
  SMOTE can be helpful to increase the size of a minority dataset but has a few limitations. If the minority class is too small SMOTE can overgeneralize. It also does not work very well with categorical data and can create some data points with values that don't make sense for the feature. There is also no way for it to predict/generate outliers which could cause a model to overfit the data.
 ### Algorithm-Level
 At the level of the algorithm there are a few approaches that can be taken to minimize the negative effects of imbalanced data.
-- 
+- Some models can weight different classes to prioritize one over another
+- The choice of model to use is important in handling imbalanced data
+	- Ensemble models such as random forests are good at handling imbalanced data