PCA Algorithm Confusion

#1

Does the svd function automatically gives eigen vectors that have maximum variance in the dataset? Like we don’t have to train it or anything?

#2

hey @nikhil_sarda ,
it doesn’t choose anything.
one parameter that we decide is number of components to be used.
based on that , it calculates the eigen vectors and then calculate explained variance using a formulae.

so to make it better , you just need to find that correct value of No. of components.

#3

So basically we get the eigen vectors with best variance automatically right?

#4

not automatically.
For example you choose the value of n_components to be 4.
so , it will get you 4 eigen vectors that represent your data. and based on those , it will tell how much is the ratio/value of explained variance in this.
In this way it works.

No to get more better PCA. You can try changing the value of components to be converted in.

#5

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