Journal of Data Science

The assignment problem, crucial in various real-world applications, involves optimizing the allocation of agents to tasks for maximum utility. While it has been well-studied in the optimization literature when the underlying utilities between all agent-task pairs are known, research is sparse when the utilities are unknown and need to be learned from data on the fly. This paper addresses this gap, as motivated by mentor-mentee matching programs at many U.S. universities. We develop an efficient sequential assignment algorithm, with the aim of nearly maximizing the overall utility simultaneously over different time periods. Our proposed algorithm is to use stochastic bandit feedback to adaptively estimate the unknown utilities through linear regression models, integrating the Upper Confidence Bound (UCB) algorithm in the multi-armed bandit problem with the Hungarian algorithm in the assignment problem. We provide theoretical bounds of our algorithm for both the estimation error and the total regret. Additionally, numerical studies are also conducted to demonstrate the practical effectiveness of our algorithm.

Society’s capacity for algorithmic problem-solving has never been greater. Artificial Intelligence is now applied across more domains than ever, a consequence of powerful abstractions, abundant data, and accessible software. As capabilities have expanded, so have risks, with models often deployed without fully understanding their potential impacts. Interpretable and interactive machine learning aims to make complex models more transparent and controllable, enhancing user agency. This review synthesizes key principles from the growing literature in this field. We first introduce precise vocabulary for discussing interpretability, like the distinction between glass box and explainable models. We then explore connections to classical statistical and design principles, like parsimony and the gulfs of interaction. Basic explainability techniques – including learned embeddings, integrated gradients, and concept bottlenecks – are illustrated with a simple case study. We also review criteria for objectively evaluating interpretability approaches. Throughout, we underscore the importance of considering audience goals when designing interactive data-driven systems. Finally, we outline open challenges and discuss the potential role of data science in addressing them. Code to reproduce all examples can be found at https://go.wisc.edu/3k1ewe.

Multivariate random forests (or MVRFs) are an extension of tree-based ensembles to examine multivariate responses. MVRF can be particularly helpful where some of the responses exhibit sparse (e.g., zero-inflated) distributions, making borrowing strength from correlated features attractive. Tree-based algorithms select features using variable importance measures (VIMs) that score each covariate based on the strength of dependence of the model on that variable. In this paper, we develop and propose new VIMs for MVRFs. Specifically, we focus on the variable’s ability to achieve split improvement, i.e., the difference in the responses between the left and right nodes obtained after splitting the parent node, for a multivariate response. Our proposed VIMs are an improvement over the default naïve VIM in existing software and allow us to investigate the strength of dependence both globally and on a per-response basis. Our simulation studies show that our proposed VIM recovers the true predictors better than naïve measures. We demonstrate usage of the VIMs for variable selection in two empirical applications; the first is on Amazon Marketplace data to predict Buy Box prices of multiple brands in a category, and the second is on ecology data to predict co-occurrence of multiple, rare bird species. A feature of both data sets is that some outcomes are sparse — exhibiting a substantial proportion of zeros or fixed values. In both cases, the proposed VIMs when used for variable screening give superior predictive accuracy over naïve measures.

Text clustering can streamline many labor-intensive tasks, but it creates a new challenge: efficiently labeling and interpreting the clusters. Generative large language models (LLMs) are a promising option to automate the process of naming text clusters, which could significantly streamline workflows, especially in domains with large datasets and esoteric language. In this study, we assessed the ability of GPT-3.5-turbo to generate names for clusters of texts and compared these to human-generated text cluster names. We clustered two benchmark datasets, each from a specialized domain: research abstracts and clinical patient notes. We generated names for each cluster using four prompting strategies (different ways of including information about the cluster in the prompt used to get LLM responses). For both datasets, the best prompting strategy beat the manual approach across all quality domains. However, name quality varied by prompting strategy and dataset. We conclude that practitioners should consider trying automated cluster naming to avoid bottlenecks or when the scale of the effort is enough to take advantage of the cost savings offered by automation, as detailed in our supplemental blueprint for using LLM cluster naming. However, to get the best performance, it is vital to test a variety of prompting strategies and perform a small test to identify which one performs best on each project’s unique data.

The US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) has begun a modernization effort to supplement survey data with non-survey data to improve estimation of agricultural quantities. As part of this effort, NASS has begun georeferencing farms on its list frame by linking geospatial data on agricultural fields with farm records on the list frame. Although many farms can be linked to geospatial data acquired by the Farm Service Agency (FSA), this linkage is not possible for farmers who do not participate in FSA programs, which may include members of some underrepresented groups in US agriculture. Thus, NASS has developed a georeferencing process for non-FSA farms, combining automatic and manual field identification, county assessor parcel data, record linkage, and classification surveys. This process serves the dual purpose of linking farms already on the list frame to geospatial data sources and identifying new farms to add to NASS’s list frame. This report evaluates the output of the non-FSA georeferencing process for 11 states, with a focus on farms added to the list frame via georeferencing. Substantial percentages (>25% for each category) of the new farms added via georeferencing were urban or suburban farms, were small, had livestock, or were in counties with Amish settlements. The georeferencing process shows promise adding farms from these groups that have historically been less well covered in NASS surveys.

Abstract: In this paper, a new class of five parameter gamma-exponentiated or generalized modified Weibull (GEMW) distribution which includes exponential, Rayleigh, Weibull, modified Weibull, exponentiated Weibull, exponentiated exponential, exponentiated modified Weibull, exponentiated modified exponential, gamma-exponentiated exponential, gamma exponentiated Rayleigh, gamma-modified Weibull, gamma-modified exponential, gamma-Weibull, gamma-Rayleigh and gamma-exponential distributions as special cases is proposed and studied. Mathematical properties of this new class of distributions including moments, mean deviations, Bonferroni and Lorenz curves, distribution of order statistics and Renyi entropy are presented. Maximum likelihood estimation technique is used to estimate the model parameters and applications to real data sets presented in order to illustrate the usefulness of this new class of distributions and its sub-models.

The presence of outliers in a dataset can substantially bias the results of statistical analyses. In general, micro edits are often performed manually on all records to correct for outliers. A set of constraints and decision rules is used to simplify the editing process. However, agricultural data collected through repeated surveys are characterized by complex relationships that make revision and vetting challenging. Therefore, maintaining high data-quality standards is not sustainable in short timeframes. The United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) has partially automated its editing process to improve the accuracy of final estimates. NASS has investigated several methods to modernize its anomaly detection system because simple decision rules may not detect anomalies that break linear relationships. In this article, a computationally efficient method that identifies format-inconsistent, historical, tail, and relational anomalies at the data-entry level is introduced. Four separate scores (i.e., one for each anomaly type) are computed for all nonmissing values in a dataset. A distribution-free method motivated by the Bienaymé-Chebyshev’s inequality is used for scoring the data entries. Fuzzy logic is then considered for combining four individual scores into one final score to determine the outliers. The performance of the proposed approach is illustrated with an application to NASS survey data.

The North American Product Classification System (NAPCS) was first introduced in the 2017 Economic Census and provides greater detail on the range of products and services offered by businesses than what was previously available with just an industry code. In the 2022 Economic Census, NAPCS consisted of 7,234 codes and respondents often found that they were unable to identify correct NAPCS codes for their business, leaving instead written descriptions of their products and services. Over one million of these needed to be reviewed by Census analysts in the 2017 Economic Census. The Smart Instrument NAPCS Classification Tool (SINCT) offers respondents a low latency search engine to find appropriate NAPCS codes based on a written description of their products and services. SINCT uses a neural network document embedding model (doc2vec) to embed respondent searches in a numerical space and then identifies NAPCS codes that are close to the search text. This paper shows one way in which machine learning can improve the survey respondent experience and reduce the amount of expensive manual processing that is necessary after data collection. We also show how relatively simple tools can achieve an estimated 72% top-ten accuracy with thousands of possible classes, limited training data, and strict latency requirements.

This paper presents an in-depth analysis of patterns and trends in the open-source software (OSS) contributions by the U.S. federal government agencies. OSS is a unique category of computer software notable for its publicly accessible source code and the rights it provides for modification and distribution for any purpose. Prompted by the Federal Source Code Policy (USCIO, 2016), Code.gov was established as a platform to facilitate the sharing of custom-developed software across various federal government agencies. This study leverages data from Code.gov, which catalogs OSS projects developed and shared by government agencies, and enhances this data with detailed development and contributor information from GitHub. By adopting a cost estimation methodology that is consistent with the U.S. national accounting framework for software investment proposed in Korkmaz et al. (2024), this research provides annual estimates of investment in OSS by government agencies for the 2009–2021 period. The findings indicate a significant investment by the federal government in OSS, with the 2021 investment estimated at around $407 million. This study not only sheds light on the government’s role in fostering OSS development but also offers a valuable framework for assessing the scope and value of OSS initiatives within the public sector.