Jan. 15, 2018

Room 201 in building 37

Machine learning methods for global model induction, like support vector machines or artificial neural networks, are nowadays applied in a wide range of data-driven applications. Therefore they appear like a natural tool also for scientific data analysis. However, although their models can reach astounding accuracies, they tend to offer surprisingly little insight into the underlying domain.

Local modeling methods address this concern by being potentially agnostic about parts of the input space in order to focus on specific effects that can be modeled in simple terms with high precision---in particular those that are unusual or outstanding given the global picture. Additionally, they achieve interpretability by using discrete symbols that correspond to meaningful notions of the discovery domain.

In this talk, I present a representative local modeling technique called subgroup discovery. I show how it was successfully used in scientific applications and discuss its computational complexity as well as practically effective algorithms.