1. A method of detecting outliers within a multidimensional data set, comprising the steps of: identifying a subset of said multidimensional data set; building a predictive model based on said identified subset; generating predicted values for each data point in said multidimensional data set based on said predictive model; repeating said identifying, building, and generating steps a predetermined number of iterations to generate a set of predicted values for each data point in said multidimensional data set based on each built predictive model; calculating an average predicted value for each data point in said multidimensional data set based on the generated predicted values; calculating the variance for each data point in said multidimensional data set; ranking the data points in said multidimensional data set based on the calculated variances; and identifying as outliers any data points in said multidimensional having variances that exceed a predetermined outlier threshold.