Hey all!
My question: Is it possible to use classification methods to determine if an unknown sample fits the distribution of known samples?
I have a known dataset that constitutes an object parameters distribution (various circles with various proprieties as circularity, area, perimeter, solidity, etc.). Rows are independent samples, and columns are each parameters. The problem is that I need the function to determine if a new sample is a circle or not. From what I saw in classification, you need to specify every class, there is no "everything else" class. What should be the best way to find if the new object is a circle or not (here circle is really just an example) and have an error or confidence measurements on the decision?
Regards,
Olivier
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