Comments (1)
The classification loss and regression loss is combined together, however, the scale of two loss is different meaning, the binary crossentropy loss is often much smaller than regression loss. Shouldn't we compensate for this difference in scale?
I agree, this could likely be a hyperparameter for the cost weights that you could tune on your specific problem.
In addition, why do we take the negative of regression loss?
This is because the model is computing the 'log probability' of the given label. When you compute the log probability of the true label against your estimated distribution, it will give you a number between negative infinity and zero. Where zero is a 'perfect' guess and negative infinity would be the worst possible guess.
Since we want to minimise our cost function, we take the negative of this so that the scale is from zero to positive infinity, and where a lower value is 'better' so that we can minimise it.
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Related Issues (7)
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