3 ms·
>Instead of training on the entire 100 million outcomes—product purchases, in this example—Mach divides them into three "buckets," each containing 33.3 million
by gambler 7y ago
>Instead of training on the entire 100 million outcomes—product purchases, in this example—Mach divides them into three "buckets," each containing 33.3 million randomly selected outcomes.
So, uh, they're doing what random forests were doing for decades? What is the key difference?
- overlords 7y agoRandom forests split the features This splits the outcomes. So each tree in RF only looks at a few features. In this, each model looks at all the features. RF can handle multiclass problems of tens to hundreds (maybe thousands). This MACH algo can handle multiclass problems of millions/billions (extreme classification).