Hi,
I am currently learning ML algorithms and implementing in R. I have a couple basic questions.
1.)Is dimensionality reduction same as feature selection? I know that in R specifying importance=T parameter in randomForest function gives you the important features based on info.gain.I was reading a bit upon PCA and came to know that it's an dimensionality reduction technique which transforms your feature space to new dimensions. How does PCA calculate the attributes importance.How to get the subset of important features using PCA in R?
2.)one of the assumption for ML algos are attributes prior to building model must be I.I.D(Identical and independently distributed). How to check about this assumption in R.
Do i need to do t.test() among all the attributes?
I may be wrong in many possible ways. please correct me if i am wrong.
Thanks,
chakravarty
I am currently learning ML algorithms and implementing in R. I have a couple basic questions.
1.)Is dimensionality reduction same as feature selection? I know that in R specifying importance=T parameter in randomForest function gives you the important features based on info.gain.I was reading a bit upon PCA and came to know that it's an dimensionality reduction technique which transforms your feature space to new dimensions. How does PCA calculate the attributes importance.How to get the subset of important features using PCA in R?
2.)one of the assumption for ML algos are attributes prior to building model must be I.I.D(Identical and independently distributed). How to check about this assumption in R.
Do i need to do t.test() among all the attributes?
I may be wrong in many possible ways. please correct me if i am wrong.
Thanks,
chakravarty