In many applications experts need to make decisions based on the analysis of multi-dimensional data. Various classification models can support the decision making process. To obtain an intuitive understanding of the classification model, interactive visualizations are essential. We argue that this is best done by a series of interactive 2D scatterplots. In this paper, we define a set of characteristics of the multi-dimensional classification model that have to be visually represented in those scatterplots. Our proposed method presents those characteristics in a uniform manner for both linear and non-linear classification methods. We combine a visualization of a Voronoi based representation of multi-dimensional decision boundaries with visualization of the distances of the data elements to these boundaries. To allow the developer of the model to refine the threshold of the classification model and instantly observe the results, we use interactive decision point selection on a performance curve. Finally, we show how the combination of those techniques allows exploration of multi-dimensional decision boundaries in 2D.

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In risk assessment applications well-informed decisions need to be made based on large amounts of multidimensional data. In many domains, not only the risk of a wrong decision, but also of the trade-off between the costs of possible decisions are of utmost importance. In this paper we describe a framework to support the decision-making process, which tightly integrates interactive visual exploration with machine learning. The proposed approach uses a series of interactive 2D visualizations of numerical and ordinal data combined with visualization of classification models. These series of visual elements are linked to the classifier's performance, which is visualized using an interactive performance curve. This interaction allows the decision-maker to steer the classification model and instantly identify the critical, cost-changing data elements in the various linked visualizations. The critical data elements are represented as images in order to trigger associations related to the knowledge of the expert. In this way the data visualization and classification results are not only linked together, but are also linked back to the classification model. Such a visual analytics framework allows the user to interactively explore the costs of his decisions for different settings of the model and, accordingly, use the most suitable classification model. More informed and reliable decisions result. A case study in the forensic psychiatry domain reveals the usefulness of the suggested approach.

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