Listen in as Anders Larson, FSA, MAAA, interviews Shea Parkes, FSA, MAAA about Random Forests. Random Forests are a very important machine learning algorithm that all practitioners should be very comfortable with. They provide great performance with minimal tuning and headache.
One of the most accessible and all-purpose tools in the predictive analytics toolkit is the Random Forest. Building on the concept of a decision tree, the Random Forest can be remarkably effective for a variety of prediction tasks. Not only is it effective for both classification and regression tasks, it’s relatively simple to implement, and generally requires less work to tune hyperparameters than other popular machine learning algorithms. In this episode, Shea Parkes and Anders Larson discuss the methodology behind the Random Forest algorithm, strengths and weaknesses, key considerations, and recommended implementations in Python and R.
