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
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.
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