Label biasing using interpolation

Abstract

Label biasing of data associated with one of a plurality of closed deals using interpolation includes retrieving, by at least one processor, training data from a data storage, and sampling, by the at least one processor, the training data periodically to capture one or more stages for each of a plurality of open deals. This also includes computing, by the at least one processor, a label for each of the plurality of open deals and a plurality of closed deals. The label is computed with a '1' or ‘0’, in some embodiments. This further training, by the at least one processor, a model on the periodically sampled training data associated with the plurality of closed deal, and generating, by the at least one processor, a score for each of the plurality of closed deal and for each of the plurality of open deals.