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Some of the customers would have bought regardless of the campaign; targeting them resulted in unnecessary costs.
Other customers were actually going to make a purchase but were ALLNET ALL128 by the campaign. The result is a loss of a sale or even a complete loss of the customer churn. In order to run a truly successful campaign, we need, instead, to be able to select customers who will buy because of the campaign, i. Uplift modeling provides a solution to this problem. The approach employs two separate training sets: The objects in the treatment dataset have been subject to some action, such as a medical treatment or a marketing campaign. The control dataset contains objects which have not been subject to the action and serve as a background against which its effect can be assessed.
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Instead of modeling class probabilities, uplift modeling attempts to model the difference between conditional class probabilities in the treatment and control groups. This way, the causal influence of the action can be modeled, and the method is able to predict the true gain with respect to taking no action from ALLNET ALL128 a given individual. To date, uplift modeling has been successfully applied in real life business settings. Ensemble methods are a class of highly successful machine learning algorithms which combine several different models to obtain an ensemble which is, hopefully, more accurate than its individual members. The goal of this paper is to evaluate selected ensemble methods in the context of uplift modeling. Our comparison will be focused on bagging and Random Forests which is a form of bagging using additional randomizationtwo very popular ensemble techniques, which, as we demonstrate, offer exceptionally good performance.
Boosting, another important technique, is beyond ALLNET ALL128 scope of this paper as adapting it to uplift modeling requires an extensive theoretical treatment and merits a separate investigation.
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Further, we provide an ALLNET ALL128 for good performance of those methods which, in our opinion, is that the nature of uplift modeling naturally leads to highly diverse ensembles. The contribution of this paper is to provide a thorough analysis of ensemble methods in the uplift modeling domain. First we discuss how various types of uplift decision trees can be combined into ensembles.
Then we provide an extensive experimental evaluation on real and artificial ALLNET ALL128 showing excellent performance of such methods. We also discuss theoretical properties of uplift ensembles and provide an explanation for their good performance based on the concept of ensemble diversity. The remaining part of the paper is organized as follows: Finally, Sect. We begin, however, by mentioning the biggest challenge one encounters when designing uplift modeling algorithms. The problem has been known in statistical literature see e. Holland as the Fundamental Problem of Causal Inference. For every individual, only ALLNET ALL128 of the outcomes is observed, after the individual has been subject to an action treated or when the individual has not been subject to the action was a control casenever both.
This is different from classification, where the true class of an individual is known, at least in the training set.
In this section we will present the related work. We begin with the motivation for uplift modeling and related techniques and a brief overview of ensemble methods, then we discuss the available uplift modeling algorithms, and finally present current references on using ensemble methods with uplift models. It presents a thorough motivation including ALLNET ALL128 use cases. The focus of those methods is, however, different from uplift modeling as their main goal is to verify the overall effectiveness of a change in website design, not selecting the right design for each customer looking into specific subgroups is usually mentioned only in the diagnostic context.
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ALLNET ALL128 is different from uplift modeling which aims at identifying groups on which a predetermined action will have the most positive effect. This is different from uplift modeling which aims at predicting this difference at the level of single records. Essentially, those methods differ by the way randomness is injected into the tree learning algorithm to ensure that models in the ensemble are diverse. As we mentioned in Sect. The quantity given in Eq. For consistency, throughout the paper, we will use the term net gain.
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In this paper we will not use the cost model, and Eq. Its obvious appeal is simplicity; however in many cases the approach may perform poorly. This is the case when the amount of training data ALLNET ALL128 large enough to accurately estimate conditional class probabilities in both groups or when the net gain is correlated with the class variable, e.
As we shall see in Sect. Other approaches to uplift modeling try to directly model the difference in conditional success probabilities between the treatment and ALLNET ALL128 groups.
Most active research follows this direction. Currently such methods are mainly adaptations of two types of machine learning algorithms: The ALLNET ALL128 approach to uplift decision tree learning has already been presented by Radcliffe et al.
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