7. Group Analysis Using Machine Learning Techniques

Analysis of performance of groups or teams is of a primary importance in field of social group studies. In this article we are targeting group performance analysis using computational techniques from machine learning. In order to understand the feature space, we make use of a combination of machine learning methods: decision trees, feature selection as well as correlation analysis. These models are chosen for their easy interpretability. Alongside we also propose methodology to build group level metrics from individual level data. This helps us interpret the feature space at group level and understand how things like attribute variety among group members affects performance. We propose a full methodology that employs machine learning models taking various group level metrics as input, finally providing a thorough analysis of the feature space. In this research we employ the NATO dataset collected using the game-based test-bed called SABRE. We give a hands-on experience by performing a four phase exhaustive group analysis on the SABRE dataset using Weka software, which is a user friendly GUI based machine learning tool.