Our latest tool development at STATWORX: random boost, an algorithm twice as fast as gradient boosting, with comparable prediction performance.
R and Python: Using reticulate to get the best of both worlds
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.
Fixing the most common problem with Plotly histograms
In today’s blog post, we show you how to improve the interactivity of Plotly histograms with automatically new rebinning.
Plotly – An interactive Charting library
In this blog we will explore the plotly library for python and R. We show how plotly is structured and use the LA Metro Bike dataset as an example to create interactive plots.
Forecasting Last Christmas Search Volume on Google Trends using Deep Learning
Last Christmas is one of the most popular Christmas tunes that were, are and will be out there. The song is written by the brilliant musician George Michael and was released in 1984, when at that time, Epic Records quickly wanted to release a Christmas tune. According to Wikipedia, there are rumours going around that George Michael just changed the …
A Performance Benchmark of Different AutoML Frameworks
In a recent blog post our CEO Sebastian Heinz wrote about Google's newest stroke of genius – AutoML Vision. A cloud service "that is able to build deep learning models for image recognition completely fully automated and from scratch". AutoML Vision is part of the current trend towards the automation of machine learning tasks. This trend started with automation of …
Learning Images with Keras
Introduction Teaching machines to handle image data is probably one of the most exciting tasks in our daily routine at STATWORX. Computer vision in general is a path to many possibilities some would consider intruiging. Besides learning images, computer vision algorithms also enable machines to learn any kind of video sequenced data. With autonomous driving on the line, learning images …
Data Science in Python – Der Einstieg in Machine Learning mit Scikit-Learn
In unseren bisherigen Artikeln zu Data Science in Python haben wir uns mit der grundlegenden Syntax, Datenstrukturen, Arrays, der Datenvisualisierung und Manipulation/Selektion auseinander gesetzt. Was jetzt noch für den Einstieg fehlt, ist die Möglichkeit Modelle auf die Daten anzuwenden, um so zum einen Muster in diese zu erkennen und zum anderen Prädiktionen abzuleiten. Die Vielfalt an implementierten Modellen in Python …
A framework to automate your work: How to set up Airflow!
In the first part of this blog post, we talked about what a DAG is, how to apply this mathematical concept in project planning and programming and why we at STATWORX decided to use Airflow compared to other workflow managers. In this part, however, we will get more technical and investigate a quite informative hello-world programming and how to set …
Comparing predictions: World Cup scores
As many others too, me and some colleagues at STATWORX took part in a little betting game for the World Cup 2018. Since the group stage is over, I was wondering how well – or better – how worse my prediction was. I am comparing my result with other predictions by using the point system of the betting game. All …