Feature selection / Dimensionality reduction for tall array

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Hi everyone!
I work with a tall array of more than 2 M observations and about 3000 numerical predictor variables. My response variable is binary (no / yes). I would like to know how and what algorithms I can use to select (or rank) the best features to develop a predictive model.

Answers (1)

Kumar Pallav
Kumar Pallav on 29 Oct 2021
Please look at the various feature selection techniques available in Statistics and Machine Learning Toolbox. As an example, you can use fscmrmr function for classification problems. Alternatively, you can use pca to reduce the dimensionality of the feature space.
Hope this helps!
Santiago Cepeda
Santiago Cepeda on 29 Oct 2021
I’m working with tall arrays so, how should I write the command?

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