I’ve recently started working with graph structures in the context of machine learning, and have found that I’ve opened what seems to be a reverse Pandora’s box, full of neat algorithms that can pull out a lot of insights from graph structures. As a way of cementing my knowledge and...
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## Quantile regression forests

A random forest is an incredibly useful and versatile tool in a data scientist’s toolkit, and is one of the more popular non-deep models that are being used in industry today. If we now want our random forests to also output their uncertainty, it would seem that we are forced...
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## Quantile regression

When we are performing regression analysis using complicated predictive models such as neural networks, knowing how certain the model is is highly valuable in many cases, for instance when the applications are within the health sector. The bootstrap prediction intervals that we covered last time requires us to train the...
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## Bootstrapping prediction intervals

Continuing from where we left off, in this post I will discuss a general way of producing accurate prediction intervals for all machine learning models that are in use today. The algorithm for producing these intervals uses bootstrapping and was introduced in Kumar and Srivastava (2012).
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## Parametric prediction intervals

One aspect of machine learning that does not seem to attract much attention is quantifying the uncertainty of our models’ predictions. In classification tasks we can partially remedy this by outputting conditional probabilities rather than boolean values, but what if the model is outputting 52%? Is that a clear-cut positive...
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