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Microsof  t Azure Machine Learning

  • Marshall Copeland
  • Julian Soh
  • Anthony Puca
  • Mike Manning
  • David Gollob

Abstract

Machine Learning emphasizes the computational work of software to process sample and/or historic data with the goal of uncovering interesting patterns, identifying objectives, and predicting outcome. For example, machine learning might uncover that for the past 14 years of worker’s compensation claims data, ear injuries in construction have an 88 percent chance of staying open for 180 days. Or when provided with juvenile offender historic data and recent juvenile crime data, machine learning might predict a 79 percent chance that a given juvenile’s next offense will result in an assault.

Keywords

Output Port Confusion Matrix Input Port Trained Model Label Column 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Marshall Copeland, Julian Soh, Anthony Puca, Mike Manning, and David Gollob 2015

Authors and Affiliations

  • Marshall Copeland
    • 1
  • Julian Soh
    • 1
  • Anthony Puca
    • 1
  • Mike Manning
    • 1
  • David Gollob
    • 1
  1. 1.COUS

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