@shahdsg4527
I used it in sign language recognition by considering the entire sequence as a single pattern
@tysonmarks2578
My understanding is that PCA compresses features into few components and that this may help improve model performance. What if stakeholders need to understand which features are most important? Can those PCA components be expanded back to their original feature forms to obtain feature importance scores?
@hengkevin6755
This awesome, now I can train my model faster 💪💪
@vidhansaini9296
And LDA
@MrDacruz19
This is 👍
@mikeplockhart
What are your thoughts on UMAP for high dimensionality data sets?
@lkd982
you make it sound so simple! :) ... so where's the catch? ;)
@squadgang1678
Is feature scaling required before pca?
@deniz-gunay
how do you deploy it after pca?
@murataavcu
Is there any version for deep learning?
@thespicycabbage
you lose transparency when you're using PCA just an FYI. Usually this is very hard to explain to stakeholders/customers and if they don't understand it, they won't like it
@rayyanamir8560
Doesn't this underfit the model?
@rahulkiroriwal8779
What about mutual info regression from skleaen.feature selection ?? Any difference in efficiency????
@Matan-Ben
It doesn't always work, if you have categorical data it won't eork
@fuba44
Sounds good, i have like 1700 features, but after reducing the dimensions, how would I infer the model? Like what is the input after reducing the dimensions?
@nimeshkumar9613