Estimating the Costs Associated with Worthwhile Predictions of Poor Air Quality

In this study we investigate the e ect of varying the ratio of false-positive and false-negative misclassi cation costs on the sensitivity and selectivity of binary predictions of exceedences of atmospheric pollutants. This allows us to determine a window of values for this ratio for which it is worthwhile making de nite rather than probabilistic predictions. The support vector machine provides a suitable statistical pattern recognition method for this work.