Do you want to optimise your code but don’t know where to start? In this post I guide you through my thought process when I optimised my code.
In our series of explaining method in 100 lines of code, we tackle random forest this time! We build it from scratch and explore it’s functions.
Could you #BeatTheAI? We let deep learning have a go at Super Mario’s first level and compared it to human players. Here we explain how we did it!
Is it cheaper to fill up your gas tank in the evening? Many car drivers have their own theories and myths about refuelling. Our colleague Jakob tackled 6 of these myths using statistics in his latest blog post.
In this blog post, we focus on automated bash/shell scripts to create docker containers. We showcase its usage with an R-shiny example.
Our latest tool development at STATWORX: random boost, an algorithm twice as fast as gradient boosting, with comparable prediction performance.
We at STATWORX use mostly R or Python for our projects. But why not both? With the help of the reticulate package we can use Python within R. Here we show an example of how to train a Support Vector Machine.
In today’s blog post, we show you how to improve the interactivity of Plotly histograms with automatically new rebinning.
This blogpost explains step by step how you can build your own Docker Image and include R scripts. With this you can have scripts running at every image’s beginning.
It’s Valentine’s day, making this the most romantic time of the year. But actually, already 2018 was a year full of love here at STATWORX: many of my STATWORX colleagues got engaged. And so we began to wonder – some fearful, some hopeful – who will be next? Therefore, today we’re going to tackle this question in the only true way: with data science!