Training Decision Trees for Optimal Decision-Making

Many analytics problems in Operations Research and the Management Sciences can be framed as decision-making problems containing uncertain input parameters to be estimated from data. For example, inventory optimization problems often require forecasts of future demand, and product recommendation systems (e.g., movies, sporting goods) depend on models for predicting customer responses to the feasible recommendations. Therefore, a question central to many analytics problems is how to optimally build models from data which estimate the uncertain inputs for the decision problems of interest. We argue that most common approaches for this task either (a) focus on the wrong objectives in training the models for the decision problem, or (b) focus on the right objectives but only study how to do so with prohibitively simple machine learning models (e.g. linear and logistic regression). In this work, we study how to train decision tree models for predicting uncertain parameters for analytical decision-making problems. Unlike other machine learning models such as linear and logistic regression, decision trees are both nonparameteric and interpretable, allowing them the capability of modeling highly complex relationships between data and predictions while also being easily visualized and interpreted. We propose tractable algorithms for decision tree training in the context of three problem domains relevant to Operations Research. First, we study how to train decision trees for delivering real-time personalized recommendations of products in settings where little prior data is available for training purposes. This problem is known in the literature as the contextual bandit problem and requires careful navigation of the so-called "exploration-exploitation trade-off" in utilizing the decision tree models. Second, we propose a new framework which we call Market Segmentation Trees (MSTs) for training decision tree models for the purposes of market segmentation and personalization. We explore several applications of MSTs relevant to personalized advertising, including recommending hotels to Expedia users as a function of their search queries and segmenting ad auctions according to the distribution of bids that they receive. Finally, we propose a general framework for training decision tree models for uncertain optimization problems which we call "SPO Trees" (SPOTs). In contrast to the typical objective of maximizing predictive accuracy, the SPOT framework trains decision trees to maximize the quality of the solutions found in the uncertain optimization problem, therefore yielding better decisions in several analytics problems of interest

Market Segmentation Trees

We seek to provide an interpretable framework for segmenting users in a population for personalized decision-making. The standard approach is to perform market segmentation by clustering users according to similarities in their contextual features, after which a "response model" is fit to each segment to model how users respond to personalized decisions. However, this methodology is not ideal for personalization, since two users could in theory have similar features but different response behaviors. We propose a general methodology, Market Segmentation Trees (MSTs), for learning interpretable market segmentations explicitly driven by identifying differences in user response patterns. To demonstrate the versatility of our methodology, we design two new, specialized MST algorithms: (i) Choice Model Trees (CMTs) which can be used to predict a user's choice amongst multiple options, and (ii) Isotonic Regression Trees (IRTs) which can be used to solve the bid landscape forecasting problem. We provide a customizable, open-source code base for training MSTs in Python which employs several strategies for scalability, including parallel processing and warm starts. We provide a theoretical analysis of the asymptotic running time of our training method validating its computational tractability on large datasets. We assess the practical performance of MSTs on several synthetic and real world datasets, showing our method reliably finds market segmentations which accurately model response behavior. Further, when applying MSTs to historical bidding data from a leading demand-side platform (DSP), we show that MSTs consistently achieve a 5-29% improvement in bid landscape forecasting accuracy over the DSP's current model. Our findings indicate that integrating market segmentation with response modeling consistently leads to improvements in response prediction accuracy, thereby aiding personalization

Classification of Autism Disorder Using Functional Connectivity Networks Obtained Through Sparse Inverse Covariance Estimation

In recent years there have been a growing number of studies investigating the efficacy of using brain connectivity networks in autism diagnosis. Many of these studies have used correlation analysis to estimate functional connectivity structure; however, this approach is problematic because it ignores the confounding effects of other brain regions. A promising area of research is using partial correlations to infer the connectivity structure. There are several methods that have recently been proposed by the machine learning community for estimating partial correlations in high-dimensional settings, such as Sparse Inverse Covariance Estimation (SICE) using the Graphical Lasso algorithm. In this study, we apply SICE in developing a connectivity-based classifier for autism disorder, and we evaluate its predictive accuracy on a set of 73 adolescents from the Autism Brain Imaging Data Exchange (ABIDE) dataset. Using SVM for our classifier, we obtained a classification error rate of 26%, with a false positive rate of 31% and a false negative rate of 22%

Nutrients and suspended sediment in snowmelt runoff from part of the upper Mississippi River basin, Minnesota and Wisconsin, 1997 /

Shipping list no.: 2001-0112-P."National Water-Quality Assessment Program."Includes bibliographical references (p. 22-23).Mode of access: Internet

Water-quality assessment of part of the upper Mississippi River Basin, Minnesota and Wisconsin : organochlorine compounds in streambed sediments and fish tissues, 1995-97 /

Shipping list no.: 2001-0308-P."Contribution from the National Water-Quality Assessment Program."Includes bibliographical references (p. 10).Mode of access: Internet

The Arabidopsis bZIP transcription factor HY5 regulates expression of the PFG1/MYB12 gene in response to light and ultraviolet-B radiation

Stracke R, Favory J-J, Gruber H, et al. The Arabidopsis bZIP transcription factor HY5 regulates expression of the PFG1/MYB12 gene in response to light and ultraviolet-B radiation. Plant Cell and Environment. 2010;33(1):88-103

Efficacy and safety of tenecteplase in combination with enoxaparin, abciximab, or unfractionated heparin: The ASSENT-3 randomised trial in acute myocardial infarction

Background: Current fibrinolytic therapies fail to achieve optimum reperfusion in many patients. Low-molecular-weight heparins and platelet glycoprotein IIb/IIIa inhibitors have shown the potential to improve pharmacological reperfusion therapy. We did a randomised, open-label trial to compare the efficacy and safety of tenecteplase plus enoxaparin or abciximab, with that of tenecteplase plus weight-adjusted unfractionated heparin in patients with acute myocardial infarction. Methods: 6095 patients with acute myocardial infarction of less than 6 h were randomly assigned one of three regimens: full-dose tenecteplase and enoxaparin for a maximum of 7 days (enoxaparin group; n=2040), half-dose tenecteplase with weight-adjusted low-dose unfractionated heparin and a 12-h infusion of abciximab (abciximab group; n=2017), or full-dose tenecteplase with weight-adjusted unfractionated heparin for 48 h (unfractionated heparin group; n=2038). The primary endpoints were the composites of 30-day mortality, in-hospital reinfarction, or in-hospital refractory ischaemia (efficacy endpoint), and the above endpoint plus in-hospital intracranial haemorrhage or in-hospital major bleeding complications (efficacy plus safety endpoint). Analysis was by intention to treat. Findings: There were significantly fewer efficacy endpoints in the enoxaparin and abciximab groups than in the unfractionated heparin group: 233/2037 (11.4%) versus 315/2038 (15.4%; relative risk 0.74 [95% CI 0.63-0.87], p=0.0002) for enoxaparin, and 223/2017 (11.1%) versus 315/2038 (15.4%; 0.72 [0.61-0.84], p<0.0001) for abciximab. The same was true for the efficacy plus safety endpoint: 280/2037 (13.7%) versus 347/2036 (17.0%; 0.81 [0.70-0.93], p=0.0037) for enoxaparin, and 287/2016 (14.2%) versus 347/2036 (17.0%; 0.84 [0.72-0.96], p=0.01416) for abciximab. Interpretation: The tenecteplase plus enoxaparin or abciximab regimens studied here reduce the frequency of ischaemic complications of an acute myocardial infarction. In light of its ease of administration, tenecteplase plus enoxaparin seems to be an attractive alternative reperfusion regimen that warrants further study