Analytics Scoring Module

The Spectrum™ Technology Platform Analytics Scoring Module provides data preparation steps that are specific to model scoring stages such as Binning Lookup, Java Model Scoring, and PMML Model Scoring. It allows predictive models defined in either QMML (Spectrum Miner's proprietary model format) or PMML (industry standard Predictive Model Markup Language) to be evaluated within dataflows and additionally allows dataflows to write and retrieve data from Miner datasets that can be consumed by the Miner predictive analytics software. It also enables the scoring and prediction of new data using binning or Java-based models that were created using Machine Learning Module stages such as Logistic Regression, K-Means Clustering, Binning, and so on. This in turn allows data to be enriched by adding predicted or scored outputs using models created by data insight teams with industry-standard data modeling tools. This may include models used to calculate the churn risk for existing customers or credit scoring models to determine a consumer's credit rating.