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Hello Yarin,
Is there any way to interpret the obtained Predictive Uncertainty(Variance)? After computing the predictive variance i.e. the sample variance of T stochastic forward passes is there any way to calculate any threshold or cutoff value so that if the predictive variance is above that value we can say that the model is uncertain or below which it is certain about its prediction? Uncertain if (predictive variance>=threshold) || Certain if (predictive variance<threshold)
something like this!
Thanks!
The text was updated successfully, but these errors were encountered:
In the field of measurement, prediction variance is seen as uncertainty, and uncertainty is used as a measure of how trustworthy the predicted data is, so there isn't a well-founded threshold, and if you want your model outputs to be as discreet as they can be, then your prediction variance needs to be as small as it can be.
Hello Yarin,
Is there any way to interpret the obtained Predictive Uncertainty(Variance)? After computing the predictive variance i.e. the sample variance of T stochastic forward passes is there any way to calculate any threshold or cutoff value so that if the predictive variance is above that value we can say that the model is uncertain or below which it is certain about its prediction?
Uncertain if (predictive variance>=threshold) || Certain if (predictive variance<threshold)
something like this!
Thanks!
The text was updated successfully, but these errors were encountered: