When trying to fit a machine learning model on a very wide data set, i.e. a data set with a large number of variables or features, it is advisable for a number of reasons to try to reduce the number of features:
- The models become easier understandable, and their output as a result better interpretable, which leads ultimately to results that can be trusted rather than those of a complex black box model.
- The exclusion of strongly correlated features can prevent model bias, as the effect of multiple variables could otherwise gain greater influence on the model as they actually do.
- Similarly, it can help to avoid the curse of dimensionality, in the case of very sparse data.
- Ultimately, the performance of the model can be optimized, as training times are shorter and the models are less computationally intensive.
While developing my talk “Machine Learning, Explainable AI, and Tableau”, that I presented together with Richard Tibbets at Tableau Conference in November 2019 in Las Vegas, I wrote a number of R scripts to perform feature selection and its preliminary tasks in Tableau. Due to the large number of questions I received about those scripts after the presentation, I decided to put together this article explaining what precisely I did there, in an attempt to make the “Tableau Feature Importance Toolbox” – as I’m calling the collection of scripts – available to the interested public. At a later point I will also summarize the contents of our talk in an article here on the blog, but for now you can find details about the scripts in the following, as well as the actual code files on my GitHub repository.
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With the integration of external services such as R, Python, and MatLab into Tableau you can significantly broaden the circle of possible users for your organization’s data science models and workflows, by embedding them into easy-to-use dashboards that are appealing to all types of consumers. That said, if you have ever worked with the integration of external services in Tableau, you will be aware that you can only define one service connection per workbook at a time – either to RServe or to TabPy. The first time this fact became painfully apparent to me was during a workshop where I was showing the integration with both services. Every single time I switched from a worksheet that employed some R code to one embedding Python code, I had to set the connection to RServe. Whenever I moved back over to another worksheet with some embedded Python code, I first ran into an error (since Tableau sent the Python code to RServe, which obviously made the R session choke) and then had to manually reset the connection to TabPy. The same was true for the session “R … You Ready for Python?” me and my colleague Lennart Heuckendorf delivered at Tableau Conference Europe 2018 and 2019. Even worse, I am more and more working with customers whose data science stack is very diverse, so they are using models in all kinds of languages. For them it is imperative to be able to run both R and Python models within the same dashboard, possibly even on one single worksheet. So I was wondering: can this be done?
tl;dr: It’s absolutely possible! This article outlines the process we suggest and guides you from zero to a working environment to do exactly this. It builds heavily on an idea proposed by my colleague Timo Tautenhahn and one of his customers here and here, so I can’t take all the credit. It also makes use of a number of external and open source software packages, so this is neither officially supported by Tableau (don’t try logging a ticket with Technical Support if this doesn’t work for you) nor is this an official Tableau tutorial. If all these caveats didn’t discourage you to try it out yourself, read on! All the code required or referenced here is available on my GitHub repository. Also, you’ll find a video at the end of this blog post to walk you through the full process.
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In the last quarter of 2016 the German marketing team came up with a great way to follow the immense success of last year’s Tableau Stadium Tour: the Tableau Cinema Tour! After visiting ten cities all over Germany, Austria, and Switzerland, we are now considering rolling it out all over Europe. Stay tuned for that! Since we often got requests for the data used in the main demo, I decided to produce this write-up of how to extract the data from the Internet Movie Database (IMDb). Unfortunately copyright reasons make it impossible for us to just provide you the ready-made data. That said, with this walk-through everybody should be able to get the data!
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Wow, another year has passed and so much has happened in the meantime!
During my job at the Institute for Transport Research at the German Aerospace Center (DLR) in Berlin I not only worked on the theoretical underpinnings and actual development and implementation of micro-scale traffic models but was obviously also involved in publicizing the results of said models and also other research work. I did this mostly with R, Shiny, PostgreSQL/PostGIS, QGIS and the occasional line of Python code sprinkled in-between. They’re all great. I love them with all my heart and enjoy every second I’m working with one of them. But I found it increasingly hard to visualize data easily and quickly while still being pretty. Sure R and
ggplot allow for camera-ready plots, Shiny and Leaflet make it increasingly easy to put together interactive plots and maps. But sometimes fiddling with their settings and writing the necessary code is just not practical to get to the point quickly. Also, during the fascinating stage of exploratory data analysis (kind of the first date with your new data in the data analysis process…) I felt focusing too much on the code and other technical aspects which distracted me from what I was originally doing: exploring my data to get a better understanding. Going back to the dating analogy it’s like over-thinking what to order and what small-talk topic to bring up next and thereby losing the interest of your possible future partner instead of being focused exclusively on him/her. Not a recipe for success… Continue reading →
The wait is finally over: the new QGIS 2.8 “Wien” has finally been released for MacOS as well! Following the (kind of) tradition of my articles showing how to install QGIS 2.6 2.4, and 2.0 on MacOS, I now sat down to write a brief walkthrough for the latest version as well.
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