1. A method creating a hierarchical learner to electronically classifying data received from a source into a hierarchical tree of categories comprising: storing and accessing pre-categorized training data wherein the pre-categorized training data comprises elements and assigned labels; accessing specified subsets of the pre-categorized training data; accessing the elements the assigned labels in the specified subsets; starting at the top of a hierarchy; creating a base learner to learn a top down model for each category of the hierarchy using a basic representation and a specified subset of the training; providing the base learner the specified subset of an entire set of pre-categorized training data as input; storing top down model output from the base learner for a category to be used as part of a prediction model for the hierarchical learner; estimating performance of the top down model learned for the category comprising; partitioning the specified subset of the data into non-overlapping subsets; for each subset, creating a stored output model comprising: providing the base learner all but that subset as training data to obtain a base model; using the top down model together with the base learner's prediction component to create a prediction for the category of every element in the subset withheld from training; storing the predictions as stored predictions; using the stored predictions over the specified subset and actual categories to assess performance of the base model; using errors committed by the base model as well as the actual categories to compute a weighting of the examples that should be used as the training data at each child of the category; storing this distribution to be used as the specified subset at the creating a base learner block; repeating a creating a block learner block for each child category of the category; using a top down prediction component that uses a stored output models to predict starting from a top-down by: predicting membership at a topmost category; for each category of which the element is predicted to be a member, predicting membership in that category's children; continuing down the hierarchy until no further categories are predicted or the bottom of the hierarchy has been reached.