Description: A look into how machine learning models implement classification and regression at a deeper level. Covers topics such as multi-regression, softmax, and ROC charts.

Description: 3rd Annual iPad contest announcement

Description: Unstructured data is often synonymous with Big Data; and the term is used and misused a lot. Richard Xu and his colleague, Dihui Lai, dispel some of the confusion about Big Data in their article “Big Data In Life Insurance—Does It Exist? If So, How Should We Handle It?” They address some of the ways to deal with the mounting challenges of capacity and speed as data scales up rapidly in size. Hadoop was once just the name of a toy elephant; but there is nothing toy-like about how it has been employed to handle very large datasets. Five exabytes supposedly represents all the words ever spoken by human beings; but according to IBM, the new SKA telescope initiative will generate over an exabyte of data every day.1 How will we cope with big data? Read Richard and Dihui’s article for some hints.

Description: Editorial for the December 2019 print edition of Predictive Analytics & Futurism newsletter describing the large number of informational resources available to PAF Section members.

Description: Chairperson's column for the May 2019 issue of Predictive Analytics & Futurism.

Description: This article introduces unsupervised methods: modeling methods that do not depend on picking an independent variable and dependent variable. Such methods are used, among other things, for determining how entities are clustered.

Description: Note from the outgoing chairperson.

Description: The new chairperson of the SOA's PAF section, Anders Larson, discusses the variety of ways in which the section helps actuaries learn about PAF.

Description: Spark offers a way to get some dramatic speed and scalability advantages over the Map-reduce methodology whenever you are doing iterative data processing and particularly when you want interactive data analysis capability. Find out why “lazy execution” can be a desirable characteristic when dealing with big data.

Description: Python & Scikit-Learn are a powerful combination for predictive modeling. Using Scikit-Learn you gain access to a variety of models, such as support vector machines, decision trees, gradient boosting and others. Scikit-Learn is very popular with the Kaggle community.