Feature selection / Dimensionality reduction for tall array

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Santiago Cepeda
Santiago Cepeda 2021-10-22
评论： Santiago Cepeda 2021-10-29
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.
Thanks.

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Kumar Pallav
Kumar Pallav 2021-10-29
Hi,
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!
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Kumar Pallav
Kumar Pallav 2021-10-29
Hi,
As an example shown here, if 'salary' is the response variable in the table 'adultdata',you could try the following command:
[idx,scores] = fscmrmr(adultdata,'salary')
Also,the data type supported for Tbl is 'table', so that may be the reason you are not able to run the syntax directly.

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