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White Paper

Data Preparation for Automated Machine Learning

The quality of predictive output relies on the quality of input — if you put good in, you’ll get good out. That’s why proper data preparation is such a critical success factor for achieving optimal machine learning results. The iterative process of preparing data for automated machine learning is both an art and a science.

In this White Paper from Jen Underwood of Impact Analytix, she will walk you through the entire machine learning lifecycle and the steps you should take to collect, prepare and understand your data.

In this report, you'll learn:

  • How to avoid overfitting and underfitting
  • The key to detecting leakage in order to find and reduce errors
  • How to improve data quality and engineer features
  • DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely – it's almost like magic!
    Omair Tariq
    Omair Tariq

    Data Analyst, Symphony Post Acute Network

  • I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
    Oliver Rees
    Oliver Rees

    General Manager – Torque Data at Virgin Australia

  • At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
    Akshay Tandon
    Akshay Tandon

    VP of Strategy Analytics, LendingTree

  • We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.
    Get Your Copy of the White Paper