Predictive Analytics
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Factorization Machines for High Cardinality Features (Part 4 of 4)
This is the fourth in a 4-part series where Anders Larson and Shea Parkes discuss predictive analytics with high cardinality features. In the prior episodes we focused on approaches to handling individual high cardinality features, but these methods did not explicitly address feature interactions. Factorization Machines can responsibly estimate all pairwise interactions, even when multiple high cardinality features are included. With a healthy selection of high cardinality features, a well tuned Factorization Machine can produce results that are more accurate than any other learning algorithm. -
Abandon the Spreadsheet and Go Digital
Lin Fangcheng argues that it is easy to digitalize by setting up new departments, but the real battle is to digitalize the incumbent departments that are predominantly spreadsheet-users. In order to reap the full potential of digitalization, we must abandon the spreadsheet and go digital. He discusses the reasons why the spreadsheet, hindered by its inherent design, has become a bottleneck for higher efficiency, and how to adopt low-code digital technology in a “together” mode. He further demonstrates that digitalization lays a solid foundation for artificial intelligence. -
Anders vs. Shea, Part 4: A Champion is Crowned
Shea Parkes, FSA, MAAA, and Anders Larson, FSA, MAAA, reveal the results of the competition and share some final thoughts on the 2021 Milliman Health Practice Hackathon. -
Anders vs. Shea, Part 2: Anders’ Story
Shea Parkes, FSA, MAAA, and Anders Larson, FSA, MAAA, are joined by Nick Vander Heyden to discuss the approach used by Anders’ team in the 2021 Milliman Health Practice Hackathon. -
Emerging Topics Community: Anders vs. Shea, Part 1: Setting the Stage
Shea Parkes, FSA, MAAA, and Anders Larson, FSA, MAAA, are are joined by the organizers of the 2021 Milliman Health Practice Hackathon: Riley Heckel, FSA, MAAA, Austin Barrington, FSA, MAAA, and Phil Ellenberg. -
Actuaries Can Excel® at Data Science (Pun Absolutely Intended)
We explore the use of mito, a Python package that allows users to use excel-like point-and-click interface with large datasets in Python. -
Hack-A-Thon
This is a historical account of the predictive analytics Hack-A-Thon sponsored by the SOA. -
Ensuring Model Wellbeing Through Monitoring
This article discusses techniques to monitor and measure predictive models for degradation in data or predictions over time. -
Back to the Futurism—New and Improved!
Feature article discussing futurism and a study that contains in-depth descriptions of the Delphi and TIA methods; listings of the rationales and thought processes, the plausible future developments that could influence the values of these four economic variables and the resulting “fan of possibilities” for the values of these variables. -
Machine Learning in the Cloud – Part 1, Intro to the Cloud
Join hosts Anders Larson, FSA, MAAA, and Shea Parkes, FSA, MAAA, for the first in a series of podcasts focused on machine learning in the cloud. This episode introduces a useful definition of the cloud and digs deeper into what aspects of machine learning make it a good fit for cloud based solutions.
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Dive into insightful content. Gain practical knowledge. Explore the latest research and key information for future use. Discover the Emerging Topics Community, an online forum that focuses on three main topic areas–Modeling, Predictive Analytics and Futurism, and Technology.