Friday, October 05, 2012
Bayesian F1
Decision theory considers the posterior probability to decide which class to chose , or weight using a cost function. However in this setting its objective is the accuracy. Most often we are more interested in more balanced measures such as F1 due to skewed classes. How would be F1 maximizer Bayesian decision mechanism if the distribution is exactly known ? I guess this is already implemented in somewhere , however I didn't face it yet.
Subscribe to:
Post Comments (Atom)
AI
Despite the benefits of AI we are starving for humanity.
-
It is difficult to create robot that cleans our arbitrary dirty dishes. However , if we put some digital information on dishes (special des...
-
Our parents were arranging their schedule according to sunrise and sunset. They are still consistent in their habit. However, in our generat...
-
Robot Particles Display: Current display devices can show any image for a very cheap price. But it will be very intresting if someone tr...
No comments:
Post a Comment