Here you will find daily news and tutorials about R, contributed by over 750 bloggers.
There are many ways to follow us - By e-mail:On Facebook: If you are an R blogger yourself you are invited to add your own R content feed to this site (Non-English R bloggers should add themselves- here)

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:

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.

The post Logistic regressions (in R) appeared first on Locke Data. Locke Data are a data science consultancy aimed at helping organisations get ready and get started with data science.

Related

To leave a comment for the author, please follow the link and comment on their blog: R – Locke Data.