Logistic regressions are a great tool for predicting outcomes that are categorical. They use a transformation function based on probability to perform a linear regression. This makes them easy to interpret and implement in other systems.

Logistic regressions can be used to perform a classification for things like determining whether someone needs to go for a biopsy. They can also be used for a more nuanced view by using the probabilities of an outcome for thinks like prioritising interventions based on likelihood to default on a loan.

I recently did a remote talk to Plymouth University on logistic regressions, which covers:

  • how they work (not maths heavy!)
  • how you build them in R
  • things to think about when preparing you data
  • ways to evaluate a logistic regression

You can watch the video below, get the slides, and view the slides’ source code.

This talk is a cut-down version of my community workshop on logistic regressions, which is in itself a cut-down version of a full day of training on them. Get in touch if you’re interested in the talk or workshop for your user group, or if you’d like to discuss in-depth training.


Logistic regressions (in R)
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