Another treat! Early access to Intro To Machine Learning videos

[radek]

@jeremy

(Just to clarify: this discussion is about the machine learning course. In the future, we should try to keep these as replies in the machine learning discussion thread.)

The curse of dimensionality isn’t meaningful in practice because out space isn’t just a bunch of meaningless cartesian coordinates. We create structure, using trees, neural nets, etc. We regularize using bagging, weight decay, dropout, etc. We find that therefore we actually can add lots of columns without seeing problems in practice.

Accidentally discovered how to quote from other threads so moving the discussion over to here

I only finished watching lesson 2 but I think what @Ekami wanted to know - what are the reasons we remove some of the columns / data in lesson 3? As it is not because we inherently have something against having that many dimensions, why do we do it?