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UtilityFunction of Classify

Pablo Gil
Posted 5 years ago

I understand that taking into account ClassifierMeassurements info on a test set, you may want to tweak how probabilities are translated to prediction, or maybe you want to prevent some misclassifications for practical reasons for example diseased being classified as healthy. However I do not understand how the utility matrix and probabilities are computed to calculate the prediction. Please could somebody explain?
POSTED BY: Pablo Gil
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Pablo Gil
Pablo Gil
Posted 5 years ago

Just in case somebody else is interested, this is a function based on Seth's answer, that computes utility values from probabilities and utilityFunction of Classify function, basically extracts values for the utility matrix and the probabilities vector, does dot product and then rebuilds the final association

utilityValues[probs_Association, uf_Association] := 
 Association[
  Thread[Keys[probs] -> (uf // Values // Values).(probs // Values)]]
POSTED BY: Pablo Gil

Here's an amended notebook that shows how you can get an (inferior) visualization in version 12.1

Attachments:
The effect of ch...nb
POSTED BY: Seth Chandler
Pablo Gil
Pablo Gil
Posted 5 years ago

Now I understand, thank you very much for your excellent work and quick answer Seth
POSTED BY: Pablo Gil

Interesting. It works in the 12.2 beta. It does not work, as you note in 12.1.1. Let me see if I can find a backwards compatible way of implementing the idea.
POSTED BY: Seth Chandler

When you apply a ClassifierFunction to a piece of data, the "decision" is the one that maximizes the expected utility of the result. Here's a Computational Essay showing how it all works. If the Community Managers could turn this into an embedded notebook I would be grateful; for some reason I can't get it to work this morning.

Attachments:
The effect of ch...nb
POSTED BY: Seth Chandler
Pablo Gil
Pablo Gil
Posted 5 years ago
POSTED BY: Pablo Gil
Rohit Namjoshi
Rohit Namjoshi
Posted 5 years ago
POSTED BY: Rohit Namjoshi
Pablo Gil
Pablo Gil
Posted 5 years ago

Thank you, interesting resource.
POSTED BY: Pablo Gil
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