Month: April 2020

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”

COVID-19: Analyze Mobility Trends with R


The global lockdown has slowed down mobility considerably. This can be seen in the data produced by our ubiquitous mobile phones.

Apple is kind enough to make those anonymized and aggregated data available to the public. If you want to learn how to get a handle on those data and analyze trends with R read on!
Continue reading “COVID-19: Analyze Mobility Trends with R”

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”