training deep leaning models let' take moment to understand these results better we started with dataset that had roughly - positive and negative outcomes considering business problemthis would translate (considering the effort from marketing teamas huge effort lost in targeting of the customers with negative outcome assume that we have customers in total (therefore positive outcomes and negative outcomestargeting each customerwe have effort units (for callsand we have successful deposits at the end howeverwith ~ precision and recallwe have the filtered list of customers whom we can target with reduced efforts thusinstead of targeting all customerswe now target just the ones we predicted positivelywhich also includes false-positives if we have positive outcomes in allthen with the preceding model having recall and precisionwe will have predicted ( )/ (with thuswe have total of ~ positive predictionswith false positives and true positives (for every predictionscomparing this with the earlier scenariofor attemptswe have successful deposits in the deep learning modelfor attempts (outcomes predicted as )we have successful deposits although there is tradeoff of losing seven positive deposits from the campaignwe have significantly reduced the effort required to achieve an almost equivalent success criteria these metrics can further be tuned based on business requirements to suit more favorable outcomes note we have not covered similar (elaborateuse case for regression readers are encouraged to experiment independently for regression use caseswhere the target variable is continuous the approach and formulation of the problem remains the samealthough the selection of loss functionthe activation for the output layerand the performance metrics would need to be based on the use case sample regression dataset that we recommend experimenting with