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!
Continue reading “Explainable AI (XAI)… Explained! Or: How to whiten any Black Box with LIME”
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)”
Happy New Year to all of you! 2020 is here and it seems that we are being overwhelmed by more and more irrationality, especially fake news and conspiracy theories.
In this post, I will give you some indication that this might actually not be the case (shock horror: good news alert!). We will be using Google Trends for that: If you want to know what Google Trends is, learn how to query it from within R and process the retrieved data, read on!
Continue reading “Psst, don’t tell anybody: The World is getting more rational!”
After my little rant (which went viral!) about the tidyverse from last week, we are going to do a little fun project in the 50’th 🙂 post of this blog: ASCII Art! If you want to have some fun by painting with letters (i.e. ASCII characters) in R and get to see a direct comparison of tidyverse and base R code, read on!
Continue reading “Painting Santa with Letters”
There seems to be some revolution going on in the R sphere… people seem to be jumping at what is commonly known as the tidyverse, a collection of packages developed and maintained by the Chief Scientist of RStudio, Hadley Wickham.
In this post, I explain what the tidyverse is and why I resist using it, so read on!
Continue reading “Why I don’t use the Tidyverse”
Modern movies use a lot of mathematics for their computer animations and CGI effects, one of them is what is known under the name fractals.
In this post, we will use this technique to create islands with coastlines that look extraordinarily realistic. If you want to do that yourself read on!
Continue reading “Create realistic-looking Islands with R”
As we have already seen in former posts simple methods can be surprisingly successful in yielding good results (see e.g Learning Data Science: Predicting Income Brackets or Teach R to read handwritten Digits with just 4 Lines of Code).
If you want to learn how some simple mathematics, known as Naive Bayes, can help you find out the sentiment of texts (in this case movie reviews) read on!
Continue reading “Learning Data Science: Sentiment Analysis with Naive Bayes”
Data Scientists know that about 80% of a Data Science project consists of preparing the data so that they can be analyzed. Building Machine Learning models is the fun part that only comes afterwards!
This process is called Data Wrangling (or Data Munging). If you want to use some Base R data wrangling techniques in a fun game to hack a password read on!
Continue reading “Learning R: Data Wrangling in Password Hacking Game”
It has been an old dream to teach a computer to see, i.e. to hold something in front of a camera and let the computer tell you what it sees. For decades it has been exactly that: a dream – because we as human beings are able to see, we just don’t know how we do it, let alone be precise enough to put it into algorithmic form.
Enter machine learning!
Continue reading “Teach R to see by Borrowing a Brain”
Customer Relationship Management (CRM) is not only about acquiring new customers but especially about retaining existing ones. That is because acquisition is often much more expensive than retention. In this post, we learn how to analyze the reasons of customer churn (i.e. customers leaving the company). We do this with a very convenient point-and-click interface for doing data science on top of R, so read on!
Continue reading “Data Science on Rails: Analyzing Customer Churn”