Cross-validation is a widely used technique to assess the generalization performance of a machine learning model. In this blog post I will introduce the basics of cross-validation, provide guidelines to tweak its parameters, and illustrate how to build it from scratch in an efficient way.
In time series context, one of most the commonly used measures is the MAPE. In this blog post, I evaluate critical arguments and weaknesses concerning the MAPE and demonstrate alternative measures.
Monotoniebedingungen können helfen den Sachverhalt besser durch Modelle darstellen zu lassen. In diesem Beitrag wird erklärt wir man solche Monotoniebedingungen in R umsetzt.
In our series of explaining method in 100 lines of code, we tackle random forest this time! We build it from scratch and explore it’s functions.
Our latest tool development at STATWORX: random boost, an algorithm twice as fast as gradient boosting, with comparable prediction performance.
Motivation There are dozens of machine learning algorithms out there. It is impossible to learn all their mechanics, however, many algorithms sprout from the most established algorithms, e.g. ordinary least squares, gradient boosting, support vector machines, tree-based algorithms and neural networks. At STATWORX we discuss algorithms daily to evaluate their usefulness for a specific project. In any case, understanding these …
Motivation There are dozens of machine learning algorithms out there. It is impossible to learn all their mechanics, however, many algorithms sprout from the most established algorithms, e.g. ordinary least squares, gradient boosting, support vector machines, tree-based algorithms and neural networks. At STATWORX we discuss algorithms daily to evaluate their usefulness for a specific project or problem. In any case, …
Machine Learning (ML) is still an underdog in the field of economics. However, it gets more and more recognition in the recent years. One reason for being an underdog is, that in economics and other social sciences one is not only interested in predicting but also in making causal inference. Thus many "off-the-shelf" ML algorithms are solving a fundamentally different …
In part one of the blog post, the Regularized Greedy Forest (RGF) was introduced as a contender to the more frequently used technique of Gradient Boosting Decision Trees (GBDT). Now it is time to turn words into actions and find out whether it actually is. Among all GBDT implementations, XGBoost is probably the most commonly used implementation in the field …
Macbeth shall never vanquish'd be until Great Birnam Wood to high Dunsinane Hill Shall come against him. (Act 4, Scene 1) In Shakespeare's The Tragedy of Macbeth, the prophecy of Birnam Wood is one of three misleading prophecies foreshadowing the defeat of the protagonist of the same name. While highly unlikely, the event of a nearby forest moving towards his …