Can’t see the forest for the trees? An intro to Random Forest machine learning in R

Chris Bowden covers everything you need to start running your own Random Forest models in R.
Thu 2 Oct, 2025 @ 12:00 pm - 1:00 pm BST
Room TBC (please check back closer to the event date) • Online via Microsoft Teams

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Talk: Can’t see the forest for the trees? An intro to Random Forest machine learning in R

Heard of Random Forests and aren’t sure where to start? Or have you always wanted to apply machine learning to a problem but are worried about the ‘black box’ nature of the models? This talk will introduce one of the most interpretable and accessible machine learning algorithms, Random Forests. Beginning with a straightforward description of the model’s structure, this talk will then cover everything you need to start running your own Random Forest models in R: from data preparation, variable selection, and types of Random Forest models — to plotting and interpreting outputs and accuracy measures. By the end of the session, you’ll be branching out into machine learning with confidence!

Speaker: Chris Bowden
Chris is a postdoctoral research associate in the Agriculture, Water and Climate group within the Civil Engineering & Management department. Starting out as a wheat breeder following his plant science background, Chris returned to his hometown of Manchester to complete his PhD in modelling crop responses to the changing monsoonal climate of India. His research is now centred on developing and applying open-source crop-water modelling tools to support assessment of risks to agriculture at basin to global scales.