Slides from Münster Data Science Meetup These are my slides from the Münster Data Science Meetup on December 12th, 2017. knitr::include_url("https://shiring.github.io/netlify_images/lime_meetup_slides_wvsh6s.pdf") My sketchnotes were collected from these two podcasts: https://twimlai.com/twiml-talk-7-carlos-guestrin-explaining-predictions-machine-learning-models/ https://dataskeptic.com/blog/episodes/2016/trusting-machine-learning-models-with-lime Sketchnotes: TWiML Talk #7 with Carlos Guestrin – Explaining the Predictions of Machine Learning Models & Data Skeptic Podcast - Trusting Machine Learning Models with Lime Example Code the following libraries were loaded: library(tidyverse) # for tidy data analysis library(farff) # for reading arff file library(missForest) # for imputing missing values library(dummies) # for creating dummy variables library(caret) # for modeling library(lime) # for explaining predictions Data The Chronic Kidney Disease dataset was downloaded from UC Irvine’s Machine Learning repository: http://archive.

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Last night, the MünsteR R user-group had another great meetup: Karin Groothuis-Oudshoorn, Assistant Professor at the University of Twente, presented her R package mice about Multivariate Imputation by Chained Equations. It was a very interesting talk and here are my sketchnotes that I took during it: MICE talk sketchnotes Here is the link to the paper referenced in my notes: https://www.jstatsoft.org/article/view/v045i03 “The mice package implements a method to deal with missing data.

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You can now book me and my 1-day workshop on deep learning with Keras and TensorFlow using R. In my workshop, you will learn the basics of deep learning what cross-entropy and loss is about activation functions how to optimize weights and biases with backpropagation and gradient descent how to build (deep) neural networks with Keras and TensorFlow how to save and load models and model weights how to visualize models with TensorBoard how to make predictions on test data Date and place depend on who and how many people are interested, so please contact me either directly or via the workshop page: https://www.

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In a recent project, I was looking to plot data from different variables along the same time axis. The difficulty was, that some of these variables I wanted to have as point plots, while others I wanted as box-plots. Because I work with the tidyverse, I wanted to produce these plots with ggplot2. Faceting was the obvious first step but it took me quite a while to figure out how to best combine facets with point plots (where I have one value per time point) with and box-plots (where I have multiple values per time point).

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I have written the following post about Predictive Maintenance and flexdashboard at my company codecentric’s blog: Predictive Maintenance is an increasingly popular strategy associated with Industry 4.0; it uses advanced analytics and machine learning to optimize machine costs and output (see Google Trends plot below). A common use-case for Predictive Maintenance is to proactively monitor machines, so as to predict when a check-up is needed to reduce failure and maximize performance.

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Working in Data Science, I often feel like I have to justify using R over Python. And while I do use Python for running scripts in production, I am much more comfortable with the R environment. Basically, whenever I can, I use R for prototyping, testing, visualizing and teaching. But because personal gut-feeling preference isn’t a very good reason to give to (scientifically minded) people, I’ve thought a lot about the pros and cons of using R.

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