Learning Statistics: Randomness is a Strange Beast


Our intuition concerning randomness is, strangely enough, quite limited. While we expect it to behave in certain ways (which it doesn’t) it shows some regularities that have unexpected consequences. In a series of seemingly random posts, I will highlight some of those regularities as well as consequences. If you want to learn something about randomness’ strange behaviour and gain some intuition read on!
Continue reading “Learning Statistics: Randomness is a Strange Beast”

Lying with Statistics: One Beer a Day will Kill you!


About two years ago the renowned medical journal “The Lancet” came out with the rather sensational conclusion that there is no safe level of alcohol consumption, so every little hurts! For example, drinking a bottle of beer per day (half a litre) would increase your risk of developing a serious health problem within one year by a whopping 7%! When I read that I had to calm my nerves by having a drink!

Ok, kidding aside: in this post, you will learn how to lie with statistics by deviously mixing up relative and absolute changes in risks, so read on!
Continue reading “Lying with Statistics: One Beer a Day will Kill you!”

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!
Continue reading “ZeroR: The Simplest Possible Classifier… or: Why High Accuracy can be Misleading”

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!
Continue reading “Learning Data Science: Understanding ROC Curves”

COVID-19 in the US: Back-of-the-Envelope Calculation of Actual Infections and Future Deaths


One of the biggest problems of the COVID-19 pandemic is that there are no reliable numbers of infections. This fact renders many model projections next to useless.

If you want to get to know a simple method how to roughly estimate the real number of infections and expected deaths in the US, read on!
Continue reading “COVID-19 in the US: Back-of-the-Envelope Calculation of Actual Infections and Future Deaths”

Contagiousness of COVID-19 Part I: Improvements of Mathematical Fitting (Guest Post)


Learning Machines proudly presents a guest post by Martijn Weterings from the Food and Natural Products research group of the Institute of Life Technologies at the University of Applied Sciences of Western Switzerland in Sion.
Continue reading “Contagiousness of COVID-19 Part I: Improvements of Mathematical Fitting (Guest Post)”

Collider Bias, or: Are Hot Babes Dim and Eggheads Ugly?


Correlation and its associated challenges don’t lose their fascination: most people know that correlation doesn’t imply causation, not many people know that the opposite is also true (see: Causation doesn’t imply Correlation either) and some know that correlation can just be random (so-called spurious correlation).

If you want to learn about a paradoxical effect nearly nobody is aware of, where correlation between two uncorrelated random variables is introduced just by sampling, read on!
Continue reading “Collider Bias, or: Are Hot Babes Dim and Eggheads Ugly?”

COVID-19: The Case of Germany

It is such a beautiful day outside, lot’s of sunshine, spring at last… and we are now basically all grounded and sitting here, waiting to get sick.

So, why not a post from the new epicentre of the global COVID-19 pandemic, Central Europe, more exactly where I live: Germany?! Indeed, if you want to find out what the numbers tell us how things might develop here, read on!
Continue reading “COVID-19: The Case of Germany”

Epidemiology: How contagious is Novel Coronavirus (2019-nCoV)?


A new invisible enemy, only 30kb in size, has emerged and is on a killing spree around the world: 2019-nCoV, the Novel Coronavirus!

It has already killed more people than the SARS pandemic and its outbreak has been declared a Public Health Emergency of International Concern (PHEIC) by the World Health Organization (WHO).

If you want to learn how epidemiologists estimate how contagious a new virus is and how to do it in R read on!
Continue reading “Epidemiology: How contagious is Novel Coronavirus (2019-nCoV)?”

Business Case Analysis with R (Guest Post)


Learning Machines proudly presents a fascinating guest post by decision and risk analyst Robert D. Brown III with a great application of R in the business and especially startup-arena! I encourage you to visit his blog too: Thales’ Press. Have fun!
Continue reading “Business Case Analysis with R (Guest Post)”