Effect of Training Data Order for Machine Learning

For many Machine Learning algorithms on supervised learning problems, the order of training data samples can affect the quality of the derived model and the accuracy of predictions. This paper describes a project to quantify this effect, and to statistically quantify the variation exhibited by several algorithms using permutations of a given training data set. It is demonstrated that this variation can be quite significant, and that training data set ordering should be an important consideration when approaching a classification task.