I have a classification problem where I want to want to use probabilities instead of classes to train my model to learn to output probabilities. In my dataset, I have instances where the probabilities of two classes are almost equal and I would like the model to be able to learn these subtleties instead of me just providing the class for each instance. Is there an ML model that can handle this?


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    $\begingroup$ The standard cross entropy formulation works fine mathematically. If the implementation you use for the loss allows for soft labels, then this should work computationally as well. $\endgroup$
    – GeoMatt22
    Feb 24 at 5:56

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