I have a question about picking the best features in a huge dataset. If I use a method like F.S, it's going to take forever. What other ways can I use besides PCA for datasets with over a million rows? Could I just pick a random part of the data and use normal methods? Thanks for the videos, they've been a big help.
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Way more exciting videos coming soon!! Nerd 🚨
Awesome!
Thanks boss!!
I have a question about picking the best features in a huge dataset. If I use a method like F.S, it's going to take forever. What other ways can I use besides PCA for datasets with over a million rows? Could I just pick a random part of the data and use normal methods? Thanks for the videos, they've been a big help.
Howdy, can you say more about the source of the data? If the data is randomly distributed then you definitely can randomly sample from the larger set.
Random add, you should consider polars instead of a pandas dataframe. Likely gonna be much more efficient.
Where are you from bro?
lol I live in California but raised in Midwest- is that what you mean?