Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation

This article presents Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. Classical partial dependence plots (PDPs) help visualize the average partial relationship between the predicted response and one or more features. In the presence of substantial interaction effects, the partial response relationship can be heterogeneous. Thus, an average curve, such as the PDP, can obfuscate the complexity of the modeled relationship. Accordingly, ICE plots refine the partial dependence plot by graphing the functional relationship between the predicted response and the feature for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate, suggesting where and to what extent heterogeneities might exist. In addition to providing a plotting suite for exploratory analysis, we include a visual test for additive structure in the data generating model. Through simulated examples and real data sets, we demonstrate how ICE plots can shed light on estimated models in ways PDPs cannot. Procedures outlined are available in the R package ICEbox.Comment: 22 pages, 14 figures, 2 algorithm

The Grapes of McGrath : The Supreme Court and the Attorney General's List of Subversive Organizations in Joint Anti-Fascist Refugee Committee v. McGrath (1951)

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73480/1/j.1540-5818.2008.00179.x.pd

The United States Flag Desecration Controversy: A Century-Long Spectacle

Controversy over desecration of the American flag has arisen periodically, particularly in times of war but also in recent years. The flag protection movement had its origins in such organizations as the Daughters and Sons of the Republic at the end of the nineteenth century, protesting commercial exploitation of the flag on the grounds that its use in advertising and political campaigns would degrade its significance. However, the concerns of the movement’s leaders may have had more to do with a fear that their traditional position of influence was being threatened by a new class of businessmen and, later, by trade unions, political radicals, and new immigrants. The recent upsurge of the debate reflects not only a collective public insecurity about the state of the country, but also a preoccupation on the part of American political leadership with symbol rather than substance.La profanation du drapeau américain soulève périodiquement la controverse, en particulier en temps de guerre, mais également depuis quelques années. Le mouvement pour la protection du drapeau tire ses origines d’organisations de la fin du XIXe siècle telles que les Daughters and Sons of the Republic, qui protestaient contre l’exploitation commerciale du drapeau sous prétexte que son emploi pour des motifs de publicité et de campagne politique en réduirait l’importance. Cependant, les inquiétudes des leaders du mouvement tenaient peut-être davantage à la crainte de voir leur influence traditionnelle menacée par une nouvelle classe d’hommes d’affaires et, plus tard, par les syndicats, les extrémistes politiques et les nouveaux immigrants. La remontée récente du débat témoigne non seulement d’un sentiment d’insécurité collective au sujet de l’état du pays, mais également de ce que les leaders politiques américains se préoccupent de symbole plutôt que de substance

Bulgaria By R. J. Crampton

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/71413/1/j.1468-229X.2008.431_55.x.pd

PhasePack: A Phase Retrieval Library

Phase retrieval deals with the estimation of complex-valued signals solely from the magnitudes of linear measurements. While there has been a recent explosion in the development of phase retrieval algorithms, the lack of a common interface has made it difficult to compare new methods against the state-of-the-art. The purpose of PhasePack is to create a common software interface for a wide range of phase retrieval algorithms and to provide a common testbed using both synthetic data and empirical imaging datasets. PhasePack is able to benchmark a large number of recent phase retrieval methods against one another to generate comparisons using a range of different performance metrics. The software package handles single method testing as well as multiple method comparisons. The algorithm implementations in PhasePack differ slightly from their original descriptions in the literature in order to achieve faster speed and improved robustness. In particular, PhasePack uses adaptive stepsizes, line-search methods, and fast eigensolvers to speed up and automate convergence

SEINE: SEgment-based Indexing for NEural information retrieval

Many early neural Information Retrieval (NeurIR) methods are re-rankers that rely on a traditional first-stage retriever due to expensive query time computations. Recently, representation-based retrievers have gained much attention, which learns query representation and document representation separately, making it possible to pre-compute document representations offline and reduce the workload at query time. Both dense and sparse representation-based retrievers have been explored. However, these methods focus on finding the representation that best represents a text (aka metric learning) and the actual retrieval function that is responsible for similarity matching between query and document is kept at a minimum by using dot product. One drawback is that unlike traditional term-level inverted index, the index formed by these embeddings cannot be easily re-used by another retrieval method. Another drawback is that keeping the interaction at minimum hurts retrieval effectiveness. On the contrary, interaction-based retrievers are known for their better retrieval effectiveness. In this paper, we propose a novel SEgment-based Neural Indexing method, SEINE, which provides a general indexing framework that can flexibly support a variety of interaction-based neural retrieval methods. We emphasize on a careful decomposition of common components in existing neural retrieval methods and propose to use segment-level inverted index to store the atomic query-document interaction values. Experiments on LETOR MQ2007 and MQ2008 datasets show that our indexing method can accelerate multiple neural retrieval methods up to 28-times faster without sacrificing much effectiveness

Use of cumulative incidence of novel influenza A/H1N1 in foreign travelers to estimate lower bounds on cumulative incidence in Mexico

Background: An accurate estimate of the total number of cases and severity of illness of an emerging infectious disease is required both to define the burden of the epidemic and to determine the severity of disease. When a novel pathogen first appears, affected individuals with severe symptoms are more likely to be diagnosed. Accordingly, the total number of cases will be underestimated and disease severity overestimated. This problem is manifest in the current epidemic of novel influenza A/H1N1. Methods and Results: We used a simple approach to leverage measures of incident influenza A/H1N1 among a relatively small and well observed group of US, UK, Spanish and Canadian travelers who had visited Mexico to estimate the incidence among a much larger and less well surveyed population of Mexican residents. We estimate that a minimum of 113,000 to 375,000 cases of novel influenza A/H1N1 have occurred in Mexicans during the month of April, 2009. Such an estimate serves as a lower bound because it does not account for underreporting of cases in travelers or for nonrandom mixing between Mexican residents and visitors, which together could increase the estimates by more than an order of magnitude. Conclusions: We find that the number of cases in Mexican residents may exceed the number of confirmed cases by two to three orders of magnitude. While the extent of disease spread is greater than previously appreciated, our estimate suggests that severe disease is uncommon since the total number of cases is likely to be much larger than those of confirmed cases