Abstract
Currently many classification algorithms exist and there is no algorithm that would outperform all the others in all tasks. Therefore it is of interest to determine which classification algorithm is the best one for a given task. Although direct comparisons can be made for any given problem using a cross-validation evaluation, it is desirable to avoid this, as the computational costs are significant. We describe a method which relies on relatively fast pairwise comparisons involving two algorithms. This method exploits sampling landmarks, that is information about learning curves besides classical data characteristics. One key feature of this method is an iterative procedure for extending the series of experiments used to gather new information in the form of sampling landmarks. Metalearning plays also a vital role. The comparisons between various pairs of algorithm are repeated and the result is represented in the form of a partially ordered ranking. Evaluation is done by comparing the partial order of algorithm that has been predicted to the partial order representing the supposedly correct result. The results of our analysis show that the method has good performance and could be of help in practical applications.

Abstract
The pursuit of unhealthy behaviors, such as smoking or binge drinking, not only carries various downside risks, but also provides pleasure. A parsimonious model, used in the literature to explain the decision to pursue an unhealthy activity, represents that decision as a tradeoff between risks and benefits. We build on this literature by surveying a rural population in South Africa to elicit the perceived riskiness and the perceived pleasure for various risky activities and to examine how these perceptions relate to the pursuit of four specific unhealthy behaviors: frequent smoking, problem drinking, seatbelt nonuse, and risky sex. We show that perceived pleasure is a significant predictor for three of the behaviors and that perceived riskiness is a significant predictor for two of them. We also show that the correlation between the riskiness rating and behavior is significantly different from the correlation between the pleasure rating and behavior for three of the four behaviors. Finally, we show that the effect of pleasure is significantly greater than the effect of riskiness in determining drinking and risky sex, while the effects of pleasure and riskiness are not different from each other in determining smoking and seatbelt nonuse. We discuss how our findings can be used to inform the design of health promotion strategies.