Decision trees are models used for the classification (prediction) of data. These models are traditionally automatically constructed. We present a system for the interactive construction and analysis of decision trees that enables domain experts to bring in domain specific knowledge. We identify different user tasks and according requirements, and develop a system incorporating a tight integration of visualization, interaction and algorithmic support. Domain experts are supported in growing, pruning, optimizing and analysing decision trees. Furthermore, we present scalable decision tree visualizations optimized for exploration.