October 14, 2019

A frequentist approach to prediction uncertainty

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Uncertainty for single predictions becomes more and more important in machine learning and is often a requirement at clients. Especially when the consequenses of a wrong prediction are high, you need to know what the probability distribution of an individual prediction is. In order to calculate this, you (I at least) immediately think about using Bayesian methods. But, these methods also have their downsides. For example, it can be computationally expensive when dealing with large amounts of data or lots of parameters. Read more

© Yu Ri Tan 2020