How to Train Generalized Additive Models (GAMs) in Model Studio скачать в хорошем качестве

How to Train Generalized Additive Models (GAMs) in Model Studio

Brian Gaines, a machine learning developer in the Analytics Division of R&D at SAS, discusses one of his favorite machine learning models, the generalized additive model, or GAM for short. In addition to providing you with an overview of GAMs and their attractive features, he will also show how easy it is to use Model Studio to train a GAM and compare it with other models. Content Outline 0:00 – Welcome 0:32 – Linear models assume each component function is linear 1:18 – GAMs use smoothing component functions to flexibly model nonlinearities 1:44 – Machine learning models face a trade-off between predictive accuracy and model interpretability 3:11 – Demo | Example: Predict mortgage default Related Resource ◉ Gaines, B. (2022). “How to Train Generalized Additive Models (GAMs) in Model Studio.” March 24. – https://communities.sas.com/t5/SAS-Co... ◉ Lamm, M., and Cai, W. (2020). “Introducing the GAMSELECT Procedure for Generalized Additive Model Selection.” In Proceedings of the SAS Global Forum 2020 Conference. Cary, NC: SAS Institute Inc. – https://www.sas.com/content/dam/SAS/s... ◉ SAS Institute Inc. (2022). SAS® Viya® Example Data Sets. Accessed February 22, 2022. – https://support.sas.com/documentation... ◉ Servén Marín, D. (2018). “pyGAM: Balancing Interpretability and Predictive Power Using Generalized Additive Models in Python.” PyData Berlin 2018. – https://github.com/dswah/PyData-Berli... SUBSCRIBE TO THE SAS USERS YOUTUBE CHANNEL #SASUsers #LearnSAS https://www.youtube.com/SASUsers?sub_... ABOUT SAS SAS is a trusted analytics powerhouse for organizations seeking immediate value from their data. A deep bench of analytics solutions and broad industry knowledge keep our customers coming back and feeling confident. With SAS®, you can discover insights from your data and make sense of it all. Identify what’s working and fix what isn’t. Make more intelligent decisions. And drive relevant change. CONNECT WITH SAS SAS ► https://www.sas.com SAS Customer Support ► https://support.sas.com SAS Communities ► https://communities.sas.com SAS Analytics Explorers ► https://explorers.sas.com Facebook ►   / sassoftware   Twitter ►   / sassoftware   LinkedIn ►   / sas   Blogs ► https://blogs.sas.com RSS ► https://www.sas.com/rss