Apache Big Data Europe 2016

Classification algorithms play an important role in different business areas, such as fraud detection, cross selling or customer behavior. In the business context, interpretability is a very desirable property, sometimes even a hard requirement. However, interpretable algorithms are usually outperformed by other non-interpretable algorithms such as Random Forest. In this talk Antonio Soriano will present a distributed implementation in Spark of the Logistic Model Tree (LMT) algorithm (Landwehr, et al. (2005). Machine Learning, 59(1-2), 161-205.), which consists of a decision tree with logistic classifiers in the leafs. While being highly interpretable, the LMT consistently performs equal or better than other popular algorithms in several performance metrics such as accuracy, precision/recall or area under the ROC curve.