8. The method of claim 1 , further comprising progressively forming said ensemble model of said dataset, using said processor, by sequentially generating a learning model for each of a successive one of said plurality of subsets, until one of: a value calculated for said at least one parameter crosses a predefined threshold that indicates that an adequate estimated learning model for the entirety of the dataset has been achieved; said processing is terminated by a user input; and all subsets of said plurality of subsets have been processed in said inductive learning model.
8. The method of claim 1 , further comprising progressively forming said ensemble model of said dataset, using said processor, by sequentially generating a learning model for each of a successive one of said plurality of subsets, until one of: a value calculated for said at least one parameter crosses a predefined threshold that indicates that an adequate estimated learning model for the entirety of the dataset has been achieved; said processing is terminated by a user input; and all subsets of said plurality of subsets have been processed in said inductive learning model. 10. The method of claim 8 , wherein, upon completing processing for a subset of data, one or more of said at least one parameter is displayed to a user, permitting said user to selectively terminate said processing of the inductive learning model if an adequate ensemble model has been achieved.