How to estimate the uncertainty of a prediction?

How to estimate the uncertainty of a prediction?

Alternatively to the accepted answer, another way to estimate the uncertainty of a specific prediction is to combine the probabilities returned by the model for each class using a certain function.

Is there a difference between uncertainty and probability?

There is no very sharp distinction in actual usage. That is, in some settings of uncertainty most people (in our culture) would think in terms of chances, in other settings most people would not, and in other settings they would be inconsistent about whether to invoke chance or not. So I believe there is no sharp philosophical distinction, either.

Is there a measure of uncertainty in classification?

If you use a standard deep neural network tpo perform classification, for example, there is no inherent measure of uncertainty. All you really have is your test accuracy, to let you know how well the model performs on hold-out data.

How does planning work in the face of uncertainty?

And planning in the face of uncertainty requires some notion of what is likely or unlikely to happen. From this argument one may assume that, built in to the common sense that all humans share, must be some intuitive notion related to likelihood. Let me make an analogy with weight.

Why is uncertainty estimation important in machine learning?

For designing machine learning (ML) models as well as for monitoring them in production, uncertainty estimation on predictions is a critical asset. It helps identify suspicious samples during model training in addition to detecting out-of-distribution samples at inference time.

How to estimate uncertainty level in deep learning?

Another more interesting way to estimate the uncertainty level for a test instance that is applicable to deep models with dropout layers is instead deep active learning [2].

How are repeated measurements used to measure uncertainty?

As shown above, repeated measurements can give you an indication of the overall uncertainty. If you want to start separating sampling, experimental, and measurement uncertainties it can be difficult. careful experimentation can get you a long way, otherwise, you can use a technique called ANOVA.

How does dropout rate affect the uncertainty of a model?

The variance of the dropout prediction is affected by the hyper parameter values quite a bit for the area with no training data. The larger the dropout rate is, the larger the variance of the dropout prediction. Nevertheless, all the considered hyper-parameter combination shows that the model is more uncertain in the region with no training data.

How is uncertainty estimation used in active learning?

This is a common practice in “Active learning”, where given a trained model you select a subset of unlabelled instances to label (to augment the initial training dataset) based on some sort of uncertainty estimation. The three most common functions used (called sampling strategies [1] in the literature) are:

Is there a way to estimate errors of prediction?

I need to estimate errors of prediction. I don’t see a way to calculate it, but is there a way to at least get a rough estimate? Where x ¯ = 1 n ∑ i x i and s x 2 = 1 n ∑ i ( x i − x ¯) 2 and similarly for y ¯, s y 2 and r x y is the correlation between x and y. Where z = x p − x ¯ s x and x p is the predictor used.

How are sampling strategies used to measure uncertainty?

The three most common functions used (called sampling strategies [1] in the literature) are: Shannon entropy: you simply apply Shannon entropy to the probabilities returned by the model for each class. The highest the entropy the highest the uncertainty.