Can’t see the forest for the trees? An intro to Random Forest ML 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!