Book Club "Machine Learning with R" - Interpretable Machine Learning

Join us for our new online boookclub session discussing ‘Hands-On Machine Learning with R’ by Bradley Boehmke and Brandon Greenwell!

Monday March 6, we will talk about interpretable machine learning
From the book:

In the previous chapters you learned how to train several different forms of advanced ML models. Often, these models are considered “black boxes” due to their complex inner-workings. However, because of their complexity, they are typically more accurate for predicting nonlinear, faint, or rare phenomena. Unfortunately, more accuracy often comes at the expense of interpretability, and interpretability is crucial for business adoption, model documentation, regulatory oversight, and human acceptance and trust. Luckily, several advancements have been made to aid in interpreting ML models over the years and this chapter demonstrates how you can use them to extract important insights.
This chapter will be presented by one of the authors of the book: Brandon Greenwell!