I would like to begin with a confession: for the primary three years of my machine studying journey, I used to be obsessive about knowledge assortment. Greater datasets meant higher fashions, proper? That’s what each weblog submit, tutorial, and convention discuss appeared to recommend. “Scale your knowledge, scale your success,” they stated.
Then I spent six months constructing a fraud detection system that acquired progressively worse as I fed it extra transaction knowledge. The mannequin that carried out fantastically on 10,000 samples was barely practical with 100,000. I used to be baffled, annoyed, and truthfully, a bit embarrassed.
That’s after I realized a counterintuitive fact that no one talks about: extra knowledge often makes your mannequin worse, not higher. And earlier than you shut this tab pondering I’ve misplaced my thoughts, let me present you precisely why this occurs and what you are able to do about it.
We stay in an period the place “large knowledge” has grow to be synonymous with “good knowledge.” Tech giants boast about their petabyte-scale datasets, analysis papers compete on dataset measurement, and knowledge scientists measure their price by the variety of rows they’ll course of.
This obsession stems from a basic misunderstanding of how machine studying truly works. The extra knowledge Amazon collects, the extra in depth and…
