This month we turn to Generalized Additive Models, a highly flexible yet still interpretable model.
Thank you to the AWS Loft (https://aws.amazon.com/start-ups/loft/ny-loft/) for hosting us. The sign-in process can be time consuming so please allow extra time to enter the building. We will try to move people inside as quickly as possible.
About the Talk:
Generalized Additive Models (GAMs) model relationships between variables as flexible splines. More flexible and powerful than linear models, but more interpretable than many machine-learning approaches, GAMs are often the best choice for predicting and understanding complex nonlinear phenomena.
R's mgcv (https://cran.r-project.org/web/packages/mgcv/index.html) is the most popular package for fitting GAMs, but many users don't know just how versatile and powerful it is. Noam will survey the features of mgcv and demonstrate how it can be used to build GAMs that deal with a great many modeling needs: modeling continuous, count, or classification outcomes, fitting spatial data or time series, variable selection, dealing with complex hierarchical structures or fitting fully bayesian models.
Noam Ross (http://www.noamross.net/) is a disease ecologist at EcoHealth Alliance (https://www.ecohealthalliance.org/), an NGO in NYC that researches the connections between human and wildlife health. Noam builds models to understand and predict disease circulation in wildlife and spillover into people. Noam is also editor for software peer review at ROpenSci (https://ropensci.org/), a developer collective that builds R packages to enable open research and data. He has a Ph.D. in ecology from the University of California-Davis (http://ecology.ucdavis.edu/). Follow him on twitter at @noamross (https://twitter.com/noamross).
Pizza (https://nyhackr.org/pizzapoll.html) begins at 6:15, the talk starts at 7, then after we head to the local bar.