Exploratory Visual System for Predictive Machine Learning of Event-Organisation Data (DAEV)

In recent years, the usage of machine learning (ML) models and especially deep neural networks (DNN) in many different domains has increased rapidly. One of the major challenges when working with ML models is to correctly and efficiently interpret the results given by a model. Additionally, understanding how the model came to its conclusions can be a very complicated task even for domain experts in the field of machine learning. For laypeople, ML models are often just black-boxes. The lack of understanding of a model and its reasoning often leads to users not trusting the model’s predictions.
In this thesis, we work with an ML model trained on event-organisation data. The goal is to create an exploratory visual event-organisation system that enables event organisers to efficiently work with the model. The main user goals in this scenario are to maximise profits and to be able to prepare for the predicted number of visitors. Tasks to achieve this goal include: finding optimal event parameters and performing what-if analyses to prepare for unexpected parameter changes e.g. change of weather. The proposed visualisation system incorporates adapted versions of multiple state-of-the-art model-agnostic interpretation methods like partial dependence plots and case-based reasoning. Since model-agnostic methods are independent of the ML model, they provide high flexibility.
Many state-of-the-art approaches to explain ML models are too complex to be understood by laypeople. Since our target group of event organisers cannot be expected to have a sufficient amount of technical knowledge, especially in the field of machine learning, one focus of this thesis is to overcome this problem. Therefore, our event-organisation system is created using a human-centred design approach performing multiple case studies with potential users during the whole development circle.