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The increased predictive power of machine learning models comes at the cost of increased complexity and loss of interpretability, particularly in comparison to parametric statistical models. This trade-off has led to the emergence of eXplainable AI (XAI) which provides methods, such as local explanations (LEs) and local variable attributions (LVAs), to shed light on how a model use predictors to arrive at a prediction. These provide a point estimate of the linear variable importance in the vicinity of a single observation. However, LVAs tend not to effectively handle association between predictors. To understand how the interaction between predictors affects the variable importance estimate, we can convert LVAs into linear projections and use the radial tour. This is also useful for learning how a model has made a mistake, or the effect of outliers, or the clustering of observations. The approach is illustrated with examples from categorical (penguin species, chocolate types) and quantitative (soccer/football salaries, house prices) response models. The methods are implemented in the R package cheem, available on CRAN. [ABSTRACT FROM AUTHOR]

Projection pursuit is used to find interesting low-dimensional projections of high-dimensional data by optimizing an index over all possible projections. Most indexes have been developed to detect departure from known distributions, such as normality, or to find separations between known groups. Here, we are interested in finding projections revealing potentially complex bivariate patterns, using new indexes constructed from scagnostics and a maximum information coefficient, with a purpose to detect unusual relationships between model parameters describing physics phenomena. The performance of these indexes is examined with respect to ideal behaviour, using simulated data, and then applied to problems from gravitational wave astronomy. The implementation builds upon the projection pursuit tools available in the R package, tourr, with indexes constructed from code in the R packages, binostics, minerva and mbgraphic. [ABSTRACT FROM AUTHOR]

This article describes structuring data and constructing plots to explore forest classification models interactively. A forest classifier is an example of an ensemble since it is produced by bagging multiple trees. The process of bagging and combining results from multiple trees produces numerous diagnostics which, with interactive graphics, can provide a lot of insight into class structure in high dimensions. Various aspects of models are explored in this article, to assess model complexity, individual model contributions, variable importance and dimension reduction, and uncertainty in prediction associated with individual observations. The ideas are applied to the random forest algorithm and projection pursuit forest but could be more broadly applied to other bagged ensembles helping in the interpretability deficit of these methods. Interactive graphics are built in R using the ggplot2, plotly, and shiny packages. [ABSTRACT FROM AUTHOR]

The article presents an overview of articles on the oil spill at the Deepwater Horizon rig by the oil firm BP PLC, which have been presented during a Data Exposition held by the American Statistical Association in 2011. It mentions the paper "Effect of Oil Spill on Birds: A Graphical Assay of the Deepwater Horizon Oil Spill's Impact on Birds" by T. Tran et al. and the paper "A Graphical Exploration of the Deepwater Horizon Oil Spill" by Lendie Follett et al.

A recent paper on visualizing the sensitivity of hadronic experiments to nucleon structure (Wang et al. in arXiv:1803.02777, 2018) introduces the tool PDFSense which defines measures to allow the user to judge the sensitivity of PDF fits to a given experiment. The sensitivity is characterized by high-dimensional data residuals that are visualized in a 3-d subspace of the 10 first principal components or using non-linear embeddings. We show how a tour, a dynamic visualisation of high dimensional data, can extend this tool beyond 3-d relationships. This approach enables resolving structure orthogonal to the 2-d viewing plane used so far, and hence finer tuned assessment of the sensitivity. [ABSTRACT FROM AUTHOR]

Since 1983, the Sections on Statistical Computing and Statistical Graphics of the American Statistical Association (ASA) have held a Data Exposition competition (usually called "Data Expo") as part of the Joint Statistical Meetings (JSM). In 2013, the focus was on data from the Knight Foundation (http://www.knightfoundation.org/), using data collected by Gallup that examined the emotional attachment of people to their community. Are there specific community attributes that attract people to certain communities, tempt them to set down roots and commit to the community for the long term? McNamara ([7]) focused on the idea of community meta-knowledge, which is essentially majority group empathy or understanding of how minorities experience their community. [Extracted from the article]

Statistical graphics play an important role in exploratory data analysis, model checking and diagnosis. With high dimensional data, this often means plotting low-dimensional projections, for example, in classification tasks projection pursuit is used to find low-dimensional projections that reveal differences between labelled groups. In many contemporary data sets the number of observations is relatively small compared to the number of variables, which is known as a high dimension low sample size (HDLSS) problem. This paper explores the use of visual inference on understanding low-dimensional pictures of HDLSS data. Visual inference helps to quantify the significance of findings made from graphics. This approach may be helpful to broaden the understanding of issues related to HDLSS data in the data analysis community. Methods are illustrated using data from a published paper, which erroneously found real separation in microarray data, and with a simulation study conducted using Amazon's Mechanical Turk. [ABSTRACT FROM AUTHOR]