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xspliner: An R Package to Build Explainable Surrogate ML Models
xspliner is an R package that helps explain black box ML models. In this presentation, you will learn what PDP curves and GLMs are and how you can calculate them based on black box models. I'll then show you a specific use-case for xspliner.

eRum 2020: Appsilon Presentations On xspliner, fast.ai, and Writing Production-Ready R Code
Appsilon engineers Krystian Igras, Marcin Dubel, and Jędrzej Świeżewski, PhD will be giving virtual presentations on Friday, June 19th. Learn about xspliner, making production-ready R code, and using R for Machine Learning projects.

COVID-19 Risk Heat Maps with Location Data, Apache Arrow, Markov Chain Modeling, and R Shiny
Our submission to the Pandemic Response Hackathon (CoronaRank) is inspired by Google’s PageRank and utilizes geolocation data in the Apache Parquet format from Veraset for effective exposure risk assessment using R and Markov Chain modeling.

Data for Good: AI for Wildlife Image Classification to Analyze Camera Trap Datasets
As part of our D4G Initiative and with the support of a Google grant, Appsilon Data Science will be contributing to to the work of biodiversity conservationists at the National Parks Agency in Gabon in collaboration with the University of Stirling.

Run a Successful ML Pilot Project in 8 Steps: How to Avoid “Pilot Purgatory”
Have you considered adding AI/ML to your organization's operations? AI models can significantly reduce costs when they are implemented properly, but can end up in "purgatory" with the wrong approach. Here's how to properly implement AI models.
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