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Foster Provost

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5 papers

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JMLR Journal 2022 Journal Article

Causal Classification: Treatment Effect Estimation vs. Outcome Prediction

Carlos Fernández-Loría
Foster Provost

The goal of causal classification is to identify individuals whose outcome would be positively changed by a treatment. Examples include targeting advertisements and targeting retention incentives to reduce churn. Causal classification is challenging because we observe individuals under only one condition (treated or untreated), so we do not know who was influenced by the treatment, but we may estimate the potential outcomes under each condition to decide whom to treat by estimating treatment effects. Curiously, we often see practitioners using simple outcome prediction instead, for example, predicting if someone will purchase if shown the ad. Rather than disregarding this as naive behavior, we present a theoretical analysis comparing treatment effect estimation and outcome prediction when addressing causal classification. We focus on the key question: "When (if ever) is simple outcome prediction preferable to treatment effect estimation for causal classification?" The analysis reveals a causal bias--variance tradeoff. First, when the treatment effect estimation depends on two outcome predictions, larger sampling variance may lead to more errors than the (biased) outcome prediction approach. Second, a stronger signal-to-noise ratio in outcome prediction implies that the bias can help with intervention decisions when outcomes are informative of effects. The theoretical results, as well as simulations, illustrate settings where outcome prediction should actually be better, including cases where (1) the bias may be partially corrected by choosing a different threshold, (2) outcomes and treatment effects are correlated, and (3) data to estimate counterfactuals are limited. A major practical implication is that, for some applications, it might be feasible to make good intervention decisions without any data on how individuals actually behave when intervened. Finally, we show that for a real online advertising application, outcome prediction models indeed excel at causal classification. [abs] [ pdf ][ bib ] &copy JMLR 2022. ( edit, beta )

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JMLR Journal 2007 Journal Article

Classification in Networked Data: A Toolkit and a Univariate Case Study

Sofus A. Macskassy
Foster Provost

This paper is about classifying entities that are interlinked with entities for which the class is known. After surveying prior work, we present NetKit, a modular toolkit for classification in networked data, and a case-study of its application to networked data used in prior machine learning research. NetKit is based on a node-centric framework in which classifiers comprise a local classifier, a relational classifier, and a collective inference procedure. Various existing node-centric relational learning algorithms can be instantiated with appropriate choices for these components, and new combinations of components realize new algorithms. The case study focuses on univariate network classification, for which the only information used is the structure of class linkage in the network (i.e., only links and some class labels). To our knowledge, no work previously has evaluated systematically the power of class-linkage alone for classification in machine learning benchmark data sets. The results demonstrate that very simple network-classification models perform quite well---well enough that they should be used regularly as baseline classifiers for studies of learning with networked data. The simplest method (which performs remarkably well) highlights the close correspondence between several existing methods introduced for different purposes---that is, Gaussian-field classifiers, Hopfield networks, and relational-neighbor classifiers. The case study also shows that there are two sets of techniques that are preferable in different situations, namely when few versus many labels are known initially. We also demonstrate that link selection plays an important role similar to traditional feature selection. [abs] [ pdf ][ bib ] &copy JMLR 2007. ( edit, beta )

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JMLR Journal 2007 Journal Article

Handling Missing Values when Applying Classification Models

Maytal Saar-Tsechansky
Foster Provost

Much work has studied the effect of different treatments of missing values on model induction, but little work has analyzed treatments for the common case of missing values at prediction time. This paper first compares several different methods---predictive value imputation, the distribution-based imputation used by C4.5, and using reduced models---for applying classification trees to instances with missing values (and also shows evidence that the results generalize to bagged trees and to logistic regression). The results show that for the two most popular treatments, each is preferable under different conditions. Strikingly the reduced-models approach, seldom mentioned or used, consistently outperforms the other two methods, sometimes by a large margin. The lack of attention to reduced modeling may be due in part to its (perceived) expense in terms of computation or storage. Therefore, we then introduce and evaluate alternative, hybrid approaches that allow users to balance between more accurate but computationally expensive reduced modeling and the other, less accurate but less computationally expensive treatments. The results show that the hybrid methods can scale gracefully to the amount of investment in computation/storage, and that they outperform imputation even for small investments. [abs] [ pdf ][ bib ] &copy JMLR 2007. ( edit, beta )

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JMLR Journal 2003 Journal Article

Tree Induction vs. Logistic Regression: A Learning-Curve Analysis

Claudia Perlich
Foster Provost
Jeffrey S. Simonoff

Tree induction and logistic regression are two standard, off-the-shelf methods for building models for classification. We present a large-scale experimental comparison of logistic regression and tree induction, assessing classification accuracy and the quality of rankings based on class-membership probabilities. We use a learning-curve analysis to examine the relationship of these measures to the size of the training set. The results of the study show several things. (1) Contrary to some prior observations, logistic regression does not generally outperform tree induction. (2) More specifically, and not surprisingly, logistic regression is better for smaller training sets and tree induction for larger data sets. Importantly, this often holds for training sets drawn from the same domain (that is, the learning curves cross), so conclusions about induction-algorithm superiority on a given domain must be based on an analysis of the learning curves. (3) Contrary to conventional wisdom, tree induction is effective at producing probability-based rankings, although apparently comparatively less so for a given training-set size than at making classifications. Finally, (4) the domains on which tree induction and logistic regression are ultimately preferable can be characterized surprisingly well by a simple measure of the separability of signal from noise. [abs] [ pdf ][ ps.gz ][ ps ]

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AAAI Conference 1998 Conference Paper

Robust Classification Systems for Imprecise Environments

Foster Provost

In real-world environments it is usually di cult to specify target operating conditions precisely. This uncertainty makes building robust classi cation systems problematic. We show that it is possible to build a hybrid classi er that will perform at least as well as the best available classi er for any target conditions. This robust performance extends across a wide variety of comparison frameworks, including the optimization of metrics such as accuracy, expected cost, lift, precision, recall, and workforce utilization. In some cases, the performance of the hybrid can actually surpass that of the best known classi er. The hybrid is also e cient to build, to store, and to update. Finally, we provide empirical evidence that a robust hybrid classi er is needed for many real-world problems.

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