This is code that accompanies a book chapter on customer churn that I have written for the German dpunkt Verlag. The book is in German and will probably appear in February: https://www.dpunkt.de/buecher/13208/9783864906107-data-science.html. The code you find below can be used to recreate all figures and analyses from this book chapter. Because the content is exclusively for the book, my descriptions around the code had to be minimal. But I’m sure, you can get the gist, even without the book.
Update: There is now a recording of the meetup up on YouTube. Here you find my slides the TWiML & AI EMEA Meetup about Trust in ML models, where I presented the Anchors paper by Carlos Guestrin et al.. I have also just written two articles for the German IT magazin iX about the same topic of Explaining Black-Box Machine Learning Models: A short article in the iX 12/2018
In our next MünsteR R-user group meetup on Tuesday, November 20th, 2018, titled Using R to help plan the future of transport, Mark Padgham will provide an overview of several inter-related R packages for analysing urban dynamics. You can RSVP here: http://meetu.ps/e/F7zDN/w54bW/f The primary motivation for developing these packages has been their use in Active Transport Futures - a group of researchers and coders striving to aid cities to better plan for futures in which active travel, particularly walking and cycling, plays an increasingly prominent role (lots of open source code at github.
A while ago, I wrote two blogposts about image classification with Keras and about how to use your own models or pretrained models for predictions and using LIME to explain to predictions. Recently, I came across this blogpost on using Keras to extract learned features from models and use those to cluster images. It is written in Python, though - so I adapted the code to R. You find the results below.
Registration is now open for my 1.5-day workshop on deep learning with Keras and TensorFlow using R. It will take place on November 8th & 9th in Munich, Germany. You can read about one participant’s experience in my workshop: Big Data – a buzz word you can find everywhere these days, from nerdy blogs to scientific research papers and even in the news. But how does Big Data Analysis work, exactly?
In our next MünsteR R-user group meetup on Tuesday, August 28th, 2018 Jenny Saatkamp will give a talk titled Blog Mining: Deriving the success of blog posts from metadata and text data. You can RSVP here: http://meetu.ps/e/F7zDN/w54bW/f In our next MünsteR Meetup, Jenny Saatkamp will present her Blog Mining analysis, which is based on 1.500 blog posts from the codecentric company blog (https://blog.codecentric.de/) and makes use of different mining techniques for metadata and text data.
This is code that will encompany an article that will appear in a special edition of a German IT magazine. The article is about explaining black-box machine learning models. In that article I’m showcasing three practical examples: Explaining supervised classification models built on tabular data using caret and the iml package Explaining image classification models with keras and lime Explaining text classification models with xgboost and lime
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