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Celmeno t1_izlxq9o wrote

What you want is a bayesian estimator. It gives you a probability distribution over all possible regression values (where the mode/expected value is the equivalent of the point estimate you are used to). The smaller the distribution the higher its estimated accuracy. You basicly get the value and its expected error all in one. No problem coding this into a neural network.

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junetwentyfirst2020 t1_izn290v wrote

Do you have a recommended read?

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Celmeno t1_izn2tmq wrote

The one book everyone practitioner of machine learning should have read: Bishop "Pattern Recognition and Machine Learning"

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junetwentyfirst2020 t1_izn67g0 wrote

I bought that book during my masters and I couldn’t figure out what Bishop was saying. It’s years later and my math is way better so I’ll give it another shot. Thank you!

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