How to Walk the Line Between Precision and Precision in Data Science

In marketing, you often need to sacrifice some precision for speed and for picking a direction. Data scientists who are trained to really adhere to the 95% will keep what's called overfitting the model. But in doing so, they're bringing a huge amount of bias into the model that did not previously exist. It's actually breaking their own fundamental values here by doing that.

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