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A Practical Introduction to Machine LearningConcepts for ActuariesAlan Chalk, FIA, MSc, and Conan McMurtrie Learning - building predictive models based on past examples - isan important part of Machine Learning and contains a vast and ever increasing array oftechniques that can be used by Actuaries alongside more traditional methods. Underlyingmany Supervised Learning techniques are a small number of important Concepts which arealso relevant to many areas of actuarial practice. In this paper we use the task of predictingaviation incident cause codes to motivate and practically demonstrate these Concepts . Theseconcepts will enable Actuaries to structure analysis pipelines to include both traditionaland modern Machine Learning techniques, to correctly compare performance and to haveincreased confidence that predictive models used are Learning ; Supervised Learning ; loss function; generalisation error;cross-validation; regularisation; feature INTRODUCTIONThis paper introduces the Machine Learning (ML) Concepts used in SupervisedLearning (building predictive models based on examples).
A Practical Introduction to Machine Learning Concepts for Actuaries Issues failures to do with the aircraft, the pilot and the procedures themselves. This demonstrates another challenge and that is, that our algorithms need to be
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