While multi-class categorization of documents has been of research interest for over a decade, relatively fewer approaches have been proposed for large scale taxonomies in which the number of classes range from hundreds of thousand as in Di- rectory Mozilla to over a million in Wikipedia. As a result of ever increasing number of text documents and images from various sources, there is an immense need for auto- matic classification of documents in such large hierarchies. In this paper, we analyze the tradeoffs between the impor- tant characteristics of different classifiers employed in the top down fashion. The properties for relative comparison of these classifiers include, (i) accuracy on test instance, (ii) training time (iii) size of the model and (iv) test time re- quired for prediction. Our analysis is motivated by the well known error bounds from learning theory, which is also fur- ther reinforced by the empirical observations on the publicly available data from the Large Scale Hierarchical Text Class- fication Challenge. We show that by exploiting the data het- erogenity across the large scale hierarchies, one can build an overall classification system which is approximately 4 times faster for prediction, 3 times faster to train, while sacrificing only 1% point in accuracy.