In cultivating newer and successively better models via lower uncertainty in each iteration the active learner seeks improving accuracy. The pareto-fronts provide a balanced trade-off of "better" solutions based on properties that directly influence performance. Between these two the active learning approach iteratively walks the trade-off between exploitation and exploration, dynamically altering the search space mappings and model structure to accomplish this balance.