As more and more businesses strive toward becoming data-driven, the use of
graph methods for storing relational data has been on the rise (
[1],
[2],
[3]).
Along with these graph databases comes more opportunities for analysing the
data, including the use of predictive machine learning models on graphs.
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Doubt - Bringing back uncertainty to ML
I have previously been exploring uncertainty measures that we can build into our machine learning models, making it easier to see whether a concrete prediction can be trusted or not. This involved confidence intervals for datasets and prediction intervals for models; see the previous posts in this series for a...
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DeepWalk
Deep learning has almost exclusively been working with simple objects: images and text. By simple I am here referring to the graphical structure of these objects, where a word is a linear sequence of letters, a document is a linear sequence of words, and an image is a rectangular grid...
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The PageRank Algorithm
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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