Have a look at what my team and I worked on during the Permafrost Hackathon in Zurich. The goal was to detect movements from multitemporal images. Since the images didn’t have any labels, we used unsupervised learning methods. Check it out, yo!
Machine Learning Goes Causal II: Meet the Random Forest’s Causal Brother
A new field of Machine Learning is born: Causal Machine Learning. Learn here about the Causal Forest, one of the most famous Causal Machine Learning algorithms for estimating heterogeneous treatment effects.
Machine Learning Goes Causal I: Why Causality Matters
A new field of Machine Learning is born: Causal Machine Learning. Learn here what it is and why it is crucial for the future of Data Science.
Is it the most wonderful time of the year?
Um herauszufinden, wie weihnachtlich die STATWORX Mitarbeiter eingestellt sind, haben wir eine kleine Umfrage zur Weihnachtsvorfreude erstellt. Dabei war es gar nicht so einfach zu entscheiden, wie die Befragten zu Weihnachtstypen zugeordnet werden sollen. Dieser Blogbeitrag zeigt die Bestimmung mittels Summenscore und Clusteranalyse und vergleicht die beiden Lösungen miteinander.
Machine Learning Interpretability @DataUniversity2019
Vom 09.-10. Oktober findet die Data University an der Goethe-Uni in Frankfurt statt, präsentiert von STATWORX & BARC.
Which Factors Influence Gas Prices? Do Gas Companies Narratives Hold True?
What are driving factors behind the gas price? With freely accessible data we are goging to find out if the brand, the location and more have any impact on the price!
What the MAPE is FALSELY blamed for, its TRUE weaknesses and BETTER alternatives!
In time series context, one of most the commonly used measures is the MAPE. In this blog post, I evaluate critical arguments and weaknesses concerning the MAPE and demonstrate alternative measures.
Monotoniebedingungen in Machine Learning Modellen mit R
Monotoniebedingungen können helfen den Sachverhalt besser durch Modelle darstellen zu lassen. In diesem Beitrag wird erklärt wir man solche Monotoniebedingungen in R umsetzt.
Simulating the bias-variance tradeoff in R
In my last blog post, I have elaborated on the Bagging algorithm and showed its prediction performance via simulation. Here, I want to go into the details on how to simulate the bias and variance of a nonparametric regression fitting method using R. These kinds of questions arise here at STATWORX when developing, for example, new machine learning algorithms or …
Optimising your R code – a guided example
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.