Apologies for a very case specific question. I have a dataset of genes, with which I am using machine learning to predict if a gene causes a disease. One of the features I have is a beta value (which is the effect size of the gene's impact on the disease), and I'm not sure how best to interpret and use this feature.

I condense the beta values from the variant level to the gene level, so a gene is left with multiple beta values like this:

Gene         Beta
ACE      -0.7, 0.1 ,0.6
NOS      0.2, 0.4, 0.5
BRCA     -0.1 ,0.1, 0.2

Currently I am trying 2 options of selecting a single beta value per gene, one where I select the absolute value per gene (and ignore whether it was a previous negative value) and another where I select the absolute value and return the previous negative numbers back to being negative. I am trying this as for beta values a postive or negative direction indicates the size of the effect a gene has on the disease, so I would think it's important to retain the negative information (as I understand it).

However, I've been advised to use just the absolute values with not retaining negative status, and I'm not sure if there's a way for me to know if one option is better than the other from the machine learning perspective. I am also having a problem in either case where my model values this feature as much more important than any other feature in my dataset. For example gradient boosting gives this an importance of 0.01, the next most important feature is at 0.001.

So my question is, how best can I interpret a highly important feature like this? If it is much more important is it actually a bias and is it likely due to my own handling/preprocessing of the feature or is it acceptable that is it just very important? Would it be possible for me to set my model to re-weight the importance of this particular feature? I have a biology background so not sure what is the normal or least biased approach.

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    $\begingroup$ Have you evaluated the importance of the beta feature only after the model training or did you also tried some feature selection strategies like chi squared? Also, you might be interested in looking at the answer to this question stats.stackexchange.com/questions/162162/… $\endgroup$ Apr 8, 2020 at 18:56
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    $\begingroup$ also, feature importance is feature importance ni the context of the model and will not indicate causality $\endgroup$
    – Victor Ng
    Apr 8, 2020 at 20:00
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    $\begingroup$ It is unclear to me how this beta value is calculated and why you get multiple values for a given gene. $\endgroup$ Apr 10, 2020 at 6:45
  • $\begingroup$ @DN1 your question is hard to understand, the explanation doesn't seem linear and it has some not well explained technical words. Could you try to re explain? $\endgroup$ Apr 12, 2020 at 9:10
  • $\begingroup$ Hi sorry for the delay in reply. I'm not sure which bits to clarify on in my question. I think Victor you have answered my main cause of confusion, if someone puts that in an answer with a credible source that I can also go look at I will give the bounty. If anyone has answers to my other general questions at the end of my post that would also help (like the possibility to manually make a model consider features at certain weights or is that biased)? $\endgroup$
    – DN1
    Apr 15, 2020 at 10:29

1 Answer 1


You can use one of 2 approaches:

The 1st is unsupervised:

Use PCA algorithm to extract the feature vectors best representing the dataset variance. The PCA algorithm extract new features which each of them is a linear combination out of the other features (independent from the label) when the 1st feature it extracts is most important feature and last one is the least important. Then you can retrieve the weights of each "Beta" value at the most important feature. Here is an example for that: https://stackoverflow.com/a/34692511/6677037

Another approach is the supervised:

using the labels, which you should use carefully and not choose the features based on the test set. With these methods you can see the most important features using "Xi Square" , "mutual information gain" etc. Then you remove the least important features. here is the easiest way to do that: https://hub.packtpub.com/4-ways-implement-feature-selection-python-machine-learning/

good luck.


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