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.
Thanks.
回答（1 个）
Kumar Pallav
2021-10-29
0 个投票
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!
3 个评论
Santiago Cepeda
2021-10-29
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.
Santiago Cepeda
2021-10-29
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