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I spent 3 amazing days at eRum conference in Budapest. The conference was a blast and organizers (BIG thanks to them again) did wonderful job compiling such a high-level event.

## My favourite talks from the conference

Better than Deep Learning – Gradient Boosting Machines (GBM) in R – Szilard Pafka

Szilard made a thorough overview of currently available ML algorithms. Showed that ML algorithm works better on tabular data than Deep Learning. Gave his advice on which packages to choose depending on your goals like maximizing speed on GPU/CPU or going to production.

My take away from his talk: you should choose algorithm based on the problem you have and take into account outside constraints like interpretability. Choosing model is one thing, but a lot of prediction improvement can come from feature engineering, so domain knowledge and problem understanding matters a lot.

Thanks to Erin LeDell’s talk we know that majority of ML tasks can be automated thanks to their awesome autoML framework.

Harness the R condition system – Lionel Henry

Lionel talked about improving errors in R. Currently R offers errors handling solely through tryCatch function. From the presentation we learn that errors are regular objects. This makes it possible for a user to provide a custom classes and error metadata, which makes it much easier to implement handling and reporting. Some of the ideas he shared will be available through the new release of lang package.

Show my your model 2.0! – Przemysław Biecek

Przemek together with Mateusz gave both a workshop and a talk about the Dalex package which is an impressive toolkit for understanding machine learning model. Dalex is being developed by talented group of Przemek’s students in Poland. Thanks to Dalex you can do single variable explanations for more than one model at the same time. It’s also easier to understand how the single variable is influencing the prediction. You may wonder why should you use Dalex if you are already familiar with Lime and the answer is: Dalex offers many methods for variable inspection (lime has one) and comparison of many methods using selected method.

The cherry on the cake was R-Ladies Budapest event where we could here 6 amazing presentations. Some of the R-Ladies were giving the second talk during those 3 days. One of them was Omayma Said talking about her Shiny app “Stringr Explorer: Tweet Driven Development for a Shiny App!”. It’s a really cool app that helps you navigate the stringr package, plus Omayma story how it was created was entertaining and admirable.

## Other conference perks

It’s always pleasure to meet in person people from R community and fellow R-Ladies.

Great things about events like this is that there is always something extra you learn: recipes package is a neat way to do data pre-processing for you model, thanks to Barbara now I know about 2 useful parameters ignoreInit and once in observEevent function and Tobias explained when would I want to choose R vs. Python when doing Deep Learning – If you just need Keras go for R, everything you can do in Python is available in R!

## Making Shiny shine brighter!

Finally I had a pleasure to give an invited talk about new Shiny packages: “Taking inspirations from proven frontend frameworks to add to Shiny with 4 6 new packages”. You can access the slides here and watch the video or YouTube. It was really valuable and motivating to get feedback on our open source packages. I’m proud that I’m part of such a great team!

If you like the idea of shiny.users and shiny.admin and would like to know when the packages are released, you can visit packages landing page or keep following our blog.

## What next?

I hope the idea of eRum will continue and others would pick it up, so we can all meet in 2 years time! I’m already jealous of all the lucky people going to useR this year. I sadly won’t be there, but Marek will and let me reveal a little secret: he will have shiny.semantic and semantic.dashboard cheat sheets and stickers to give away!

Read the original post at Appsilon Data Science Blog.

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