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Data Science Fails: There’s No Such Thing as a Free Lunch

Podcast

Humans can be biased in choosing algorithms. Sometimes they prefer to run a particular type of algorithm that they are comfortable with. In the data science community, there’s often a lot of hype surrounding the latest algorithm, whether it be about “deep learning” or a “decision jungle.” All of this hype and attention can also trigger human bias. While this podcast isn’t about sandwiches, it is related to lunch, specifically the no free lunch theorem. In short, the theorem states that no algorithm can be equally good at learning everything, which means that you can’t know in advance which algorithm will work best on your data.

In this 15-minute podcast produced by Data Science Central, you will learn the science of cognitive bias in data science, how it leads to substandard AIs and models, and how to avoid this type of data science fail.

Speaker

colin priest
Colin Priest

VP, AI Strategy, DataRobot

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    Omair Tariq

    Data Analyst, Symphony Post Acute Network

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    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

  • DataRobot allows us to understand the data that’s being fed into our models without blindly feeding whatever we get into our system. DataRobot makes my team very effective.
    Deena Narayanaswamy
    Deena Narayanaswamy

    Head of Data Insights, Avant

  • 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.
    Evariant