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.