Author: Learning Machines

Feed: R-bloggers. Author: Learning Machines. More and more decisions by banks on who gets a loan are being made by artificial intelligence. The terms being used are credit scoring and credit decisioning. They base their decisions on models whether the customer will pay back the loan or will default, i.e. determine their creditworthiness. If you want to learn how to build such a model in R yourself (with the latest R ≥ 4.1.0 syntax as a bonus), read on! As always we need data to build our model. In this case we will use credit scoring data from a kaggle ... Read More

Feed: R-bloggers. Author: Learning Machines. Public-key cryptography is one of the foundations of our modern digital life. Normally it is quite hard to understand but with our literally colourful explanation it is a walk in the park. At the end we also give the nerd version, so read on! One problem with cryptography is that you often would like to have a shared secret key to encrypt and decrypt messages (e.g. credit card information) but you of course cannot just send the secret key to the other party unencrypted – now you see the problem! We want to find a ... Read More

Feed: R-bloggers. Author: Learning Machines. A short one for today: in this post we will learn how to easily create truth tables with R and will contribute our code to the growing repository of Rosetta code. I hope that you will learn a few tricks along the way, so read on! We have covered bits of code that I contributed to Rosetta Code on this blog before (see Category: Rosetta Code). This time we want to solve the following task: Truth tableA truth table is a display of the inputs to, and the output of a Boolean function organized as ... Read More

Feed: R-bloggers. Author: Learning Machines. I sometimes joke that as an Aries I don’t believe in zodiac signs. But could there still be some pattern, e.g. in the sense that people born in spring are more prone to success than those born during the winter months? In this post, we will provide a definitive answer with one of the most fascinating datasets I have ever encountered, so read on! The data we will be using is from the extraordinary Pantheon project: Pantheon is an observatory of collective memory focused on biographies with a presence in at least 15 languages in ... Read More

Feed: R-bloggers. Author: Learning Machines. This is our 101’st blog post here on Learning Machines and we have prepared something very special for you! Oftentimes the different concepts of data science, namely artificial intelligence (AI), machine learning (ML), and deep learning (DL) are confused… so we asked the most advanced AI in the world, OpenAI GPT-3, to write a guest post for us to provide some clarification on their definitions and how they are related. We are most delighted to present this very impressive (and only slightly redacted) essay to you – enjoy! Artificial intelligence (AI), machine learning (ML), and ... Read More

Feed: R-bloggers. Author: Learning Machines. My father-in-law used to write down the numbers drawn on the lottery to find patterns, especially whether some numbers were “due” because they hadn’t been drawn for a long time. He is not alone! And don’t they have a point? Shouldn’t the numbers balance after some time? Read on to find out! The perceived paradox arises because long streaks of supposedly random events (like getting red in roulette) are extremely unlikely yet that doesn’t change the probability of the next draw. To think differently is known as the so-called gambler’s fallacy (a.k.a. Monte Carlo fallacy) ... Read More

Feed: R-bloggers. Author: Learning Machines. Some time ago I conducted a poll on LinkedIn that quickly went viral. I asked which of three different coin tossing sequences were more likely and I received exactly 1,592 votes! Nearly 48,000 people viewed it and more than 80 comments are under the post (you need a LinkedIn account to fully see it here: LinkedIn Coin Tossing Poll). In this post I will give the solution with some background explanation, so read on! Please have a look at the original question and the final result: The majority (exactly 1,000 people!) got it right, all ... Read More

Feed: R-bloggers. Author: Learning Machines. In 2018 the renowned scientific journal science broke a story that researchers had re-engineered the commercial criminal risk assessment software COMPAS with a simple logistic regression (Science: The accuracy, fairness, and limits of predicting recidivism). According to this article, COMPAS uses 137 features, the authors just used two. In this post, I will up the ante by showing you how to achieve similar results using just one simple rule based on only one feature which is found automatically in no-time by the OneR package, so read on! Algorithms for predicting recidivism are more and more ... Read More

Feed: R-bloggers. Author: Learning Machines. Wikipedia defines Parrondo’s paradox in game theory as A combination of losing strategies becomes a winning strategy. If you want to learn more about this fascinating topic and see an application in finance, read on! Please have a look at this wonderful little video by my colleague Professor Humberto Barreto from DePauw University, Indiana, based on a now-defunct app from Alexander Bogomolny of the well-known maths site “Cut the Knot”. It illustrates the general idea of Parrondo’s paradox in about 2 minutes: Now, imagine that we could do something similar in finance: combine two losing ... Read More

Feed: R-bloggers. Author: Learning Machines. The German news magazine DER SPIEGEL has a regular puzzle section in its online version, called “Rätsel der Woche” (“Riddle of the Week”). Some of those puzzles are quite interesting but I am often too lazy to solve them analytically. So I often kill two birds with one stone: having fun solving the puzzle with R and creating some new teaching material for my R classes! This is what we will do with one of those more interesting riddles, which is quite hard to solve analytically but relatively easy to solve with R, so read ... Read More