Question #14

Out-of-sample error equals bias error plus variance error plus base error. Bias error is the extent to which a model fits the training data. Variance error describes the degree to which a model's results change in response to new data from validation and test samples. Base error comes from randomness in the data. (Module 3.1, LOS 3.b) Hanna Kowalski is a senior fixed-income portfolio analyst at Czarnaskala BP. Kowalski supervises Lena Nowak, who is a junior analyst. Over the past several years, Kowalski has become aware that investment firms are increasingly using technology to improve their investment decision making. Kowalski has become particularly interested in machine learning techniques and how they might be applied to investment management applications. Kowalski has read a number of articles about machine learning in various journals for financial analysts. However, she has only a minimal knowledge of how she might source appropriate model inputs, interpret model outputs, and translate those outputs into investment actions. Kowalski and Nowak meet to discuss plans for incorporating machine learning into their investment model. Kowalski asks Nowak to research machine learning and report back on the types of investment problems that machine learning can address, how the algorithms work, and what the various terminology means. After spending a few hours researching the topic, Nowak makes a number of statements to Kowalski on the topics of: Classification and regression trees (CART) Hierarchical clustering Neural networks Reinforcement learning (RL) algorithms Kowalski is left to work out which of Nowak's statements are fully accurate and which are not.