Cupid’s Arrow: How to Boost your Chances at Speed Dating!


During our little break, Valentine’s Day was celebrated. Yet for many, it was a depressing day because they are single and are looking for love.

Speed dating is a popular format (in times of Covid-19 also in virtual form) to meet many different potential soul mates in a short period of time. If you want to learn which factors determine “getting to the next round”, read on!
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Why Gradient Descent Works (and How To Animate 3D-Functions in R)


The workhorse of Machine Learning is Gradient Descent. If you want to understand how and why it works and, along the way, want to learn how to plot and animate 3D-functions in R read on!
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OneR in Medical Research: Finding Leading Symptoms, Main Predictors and Cut-Off Points


We already had a lot of examples that make use of the OneR package (on CRAN), which can be found in the respective Category: OneR.

Here we will give you some concrete examples in the area of research on Type 2 Diabetes Mellitus (DM) to show that the package is especially well suited in the field of medical research, so read on!
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Logistic Regression as the Smallest Possible Neural Network


We already covered Neural Networks and Logistic Regression in this blog.

If you want to gain an even deeper understanding of the fascinating connection between those two popular machine learning techniques read on!
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Time Series Analysis: Forecasting Sales Data with Autoregressive (AR) Models


Forecasting the future has always been one of man’s biggest desires and many approaches have been tried over the centuries. In this post we will look at a simple statistical method for time series analysis, called AR for Autoregressive Model. We will use this method to predict future sales data and will rebuild it to get a deeper understanding of how this method works, so read on!
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Will AI become conscious any time soon?


We all know the classical Sci-Fi trope of intelligent machines becoming conscious and all the potential ramifications that could follow from there (free will, fighting their human creators, ethical dilemmas, and so forth). Now, is this a realistic scenario? As a researcher in the area of AI (see e.g. So, what is AI really?), with a penchant for philosophy, I share my thoughts here with you, so read on!
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ZeroR: The Simplest Possible Classifier… or: Why High Accuracy can be Misleading


In one of my most popular posts So, what is AI really? I showed that Artificial Intelligence (AI) basically boils down to autonomously learned rules, i.e. conditional statements or simply, conditionals.

In this post, I create the simplest possible classifier, called ZeroR, to show that even this classifier can achieve surprisingly high values for accuracy (i.e. the ratio of correctly predicted instances)… and why this is not necessarily a good thing, so read on!
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Learning Data Science: Understanding ROC Curves


One widely used graphical plot to assess the quality of a machine learning classifier or the accuracy of a medical test is the Receiver Operating Characteristic curve, or ROC curve. If you want to gain an intuition and see how they can be easily created with base R read on!
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The One Question you should ask your Partner before Marrying!


Valentine’s Day is around the corner and love is in the air… but, shock horror, nearly every second marriage ends in a divorce! Unfortunately, I can tell you first hand that this is an experience you’d rather not have. In this post, we see how data science, in the form of the OneR package and an interesting new data set, might potentially help you to avoid that tragedy… so read on!
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Explainable AI (XAI)… Explained! Or: How to whiten any Black Box with LIME


We already covered the so-called Accuracy-Interpretability Trade-Off which states that oftentimes the more accurate the results of an AI are the harder it is to interpret how it arrived at its conclusions (see also: Learning Data Science: Predicting Income Brackets).

This is especially true for Neural Networks: while often delivering outstanding results, they are basically black boxes and notoriously hard to interpret (see also: Understanding the Magic of Neural Networks).

There is a new hot area of research to make black-box models interpretable, called Explainable Artificial Intelligence (XAI), if you want to gain some intuition on one such approach (called LIME), read on!
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