I have a dataset that has 1216 columns and 104 observations. I want to somehow quantify numerically, how much each of the columns influence a change (drop or raise) in the value of the target variable, or at least, get to know which of the columns have the most influence over the target.

At a first attempt i have thought about simply interpreting the coefficients of a linear regression model, but since there are many more variables than observations, the model will achieve a perfect fit and i don't know if in this conditions the coefficients are meaningful to determine the influence of the variables over the target.

I have also thought about lasso and ridge regression, but these methods forces the coefficientes to be near 0, so how could i know in which measure a variable influences the target if the coefficients are 0 or near 0?

My question is, which approach should i use here? Which method would be more helpful to determine and quantify in a meaningful way how much a variable is influencing the target?

I'm using Python for this analysis so if you you could point or suggest a method that can be implemented in Python it would also be very helpful.

Thank you very much in advance.


I would try a PCA. As you should know that a PC is a linear combination of one or more original features.

Once I have fitted the model on PCs and got the feature importances/coefficients, I would use it to proportionately allocate among the individual members of the PCs.

  • $\begingroup$ Hi @jdsuryap. Thank you for your answer. Could you give a little more detail about the last part of your answer? How many principal components would be more appropriate to use in this case? And when you say: "I would use it to proportionately allocate among the individual members of the PCs" how could i exactly allocate these importances to each member of the PC's? Thank you $\endgroup$ – Miguel 2488 Apr 18 at 14:08
  • $\begingroup$ Hi @Miguel2488, the optimum number of PCs should be guided by using elbow method using a metric like explained variance, something like in this post stats.stackexchange.com/questions/22569/…. Also, regarding "how could i exactly allocate these importances" - I would use the weights of the original features in the PCs to allocate the importance/coefficient of the PCs. $\endgroup$ – jdsuryap Apr 18 at 15:02

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.