Algorithms, Modeling Methods, Verification & Validation

Learn how to use the basics of predictive analytics and modeling data to determine which algorithms to use. Understand the similarities and differences and which options affect the models most. Discover how to verify and validate your model. Topics covered include predictive analytics algorithms for supervised learning, including decision trees, linear and logistic regression, neural networks, k-nearest neighbor, support vector machines, and model ensembles. Gain a deeper understanding of how algorithms work qualitatively by reviewing best practices and the influence of various options on predictive models. Prerequisites: I&C SCI X425.61 Introduction to Predictive Analytics and I&C SCI X425.63 Effective Data Preparation.