Publications by Gary Hutson

TidyModels or CARET – how they compare?

07.10.2021

People have been telling me not to teach caret anymore, albeit I do teach TidyModels as well. Their argument is that TidyModels is new and shiny and we should be using the most up to date tools. I get this justification, but is caret really dead? My answer is “not by a long shot!“. Hats off to Max Kuhn and the team for rolling out these aweso...

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What a NHS-R Community Conference it was – simply wow!

12.11.2021

The NHS-R Virtual Conference concluded this week and we have had a number of excellent speakers from the Health & Social Care sector, alongside working with key partners of interest. It kicked off with a full day jammed packed of speakers from all over the NHS. The second day we had international speakers from the US, Australia and other European...

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TidyModels hands on for the NHS-R community – building a supervised ML classification model from scratch

16.12.2021

The inclusive NHS-R community have welcomed me to complete my webinar hat trick for this year. In this webinar I focus on building a ML model from scratch. The overview of the webinar is: Build your first Machine Learning classification model with tidymodelsUnderstanding data processing for machine learningEvaluate your machine learning models wi...

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A package of Machine Learning datasets has arrived for R – MLDataR

05.01.2022

I am proud to announce my seventh package for you lovely R folks. This time it is a collection of datasets aimed at giving people in healthcare, and beyond, some solid examples for using with R. There remains a few excellent packages for this, such as mlbench, but they are limited in the number of datasets you can derive. Using the package To us...

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SangerTools – a R package for working with population health datasets has arrived

21.02.2022

Asif Laldin was the main developer of this package, but I contributed a few functions and offered overall package development advice. This is a great effort and is crammed full of functions for working with population health datasets, standardised rates, standard visuals, etc. It contains a smorgasbord of delightful functions to wet any R develop...

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