When evaluating binary classification algorithms it is a good idea to have a baseline for the performance measures. In this blog post I calculate the classification performance of really dumb classifiers. These models do not use any feature information. If your own classification model performs just like them, there is a problem.
Massive open online courses (MOOCs) did not revolutionize education. Why? They suffer from abysmal completion rates. Most students start a MOOC without finishing it. In this blog post I take a look at what my own company's e-learning course completion rates would be if we offered standard MOOCs.
Using rating data to predict how much people will like a product is more tricky than it seems. Even though ratings often get treated as if they were a kind of measurement, they are actually a ranking. The difference is not just academic. In this blog post I show how using an appropriate model for such data improves prediction accuracy.
Information with a geographical element can best be visualised with a map. However, big regions tend to dominate maps independent of their actual importance. I show possible ways around this issue and let you generate the right data map for your own purposes without needing to code.
2018 was a wild summer for the rental bike market in Berlin. Many new bike systems pushed into the market in the beginning. By now, two have already left. In between, I counted every rented bike I saw. Which bikes got rented the most?
Using a machine learning algorithm out of the box is problematic when one class in the training set dominates the other. Synthetic Minority Over-sampling Technique (SMOTE) solves this problem. In this tutorial I'll walk you through how SMOTE works and then how the SMOTE function code works.
Is it worth organising your data in a data base if all you are interested in is speed? It depends on what you are doing with the data. This guide teaches you where to expect speed advantages of SQLite and R.