Analytics Scoring Module Components

The Analytics Scoring Module consists of the following components.

  • Binning Lookup—This stage can be used to apply previously defined binning to new data using existing bins created in dataflows using the Machine Learning Module Binning stage.
  • Java Model Scoring—This stage can be used score new data using the formula created when you fit a machine learning model.
  • PMML Model Scoring—This stage can be used to evaluate any model stored in the Analytics Scoring Repository in the context of a dataflow.
  • Read from Miner Dataset—This stage can be used to read data from a focus file to be used within a dataflow.
  • Write to Miner Dataset—This stage can be used to write data from a dataflow to a focus file.
  • Machine Learning Model Management—This repository includes Model Assessment, where you manage all machine learning models on your Spectrum™ Technology Platform server, and Binning Management, where you manage all binning on your Spectrum™ Technology Platform server.
  • Analytics Scoring Repository —This is the central repository for all models available to the Analytics Scoring Module. Users can manage the repository via a web client.

See the Data Science Demonstration Flows in the Machine Learning Guide for examples of supervised and unsupervised learning that include the scoring of data using Java Model Scoring.