Benchmark Experiments A Tool for Analyzing Statistical Learning Algorithms

Benchmark experiments nowadays are the method of choice to evaluate learn-ing algorithms in most research fields with applications related to statistical learning. Benchmark experiments are an empirical tool to analyze statistical learning algorithms on one or more data sets: to compare a set of algorithms, to find the best hyperparameters for an algorithm, or to make a sensitivity analy-sis of an algorithm. In the main part, this dissertation focus on the comparison of candidate algorithms and introduces a comprehensive toolbox for analyzing such benchmark experiments. A systematic approach is introduced – from ex-ploratory analyses with specialized visualizations (static and interactive) via formal investigations and their interpretation as preference relations through to a consensus order of the algorithms, based on one or more performance mea-sures and data sets. The performance of learning algorithms is determined by data set characteristics, this is common knowledge. Not exactly known is the concrete relationship between characteristics and algorithms. A formal frame