I have 33 variables my dataset, I need to omit some less significant features then, which is the "best suitable feature selection method " for the Ordinal Logistic Regression?

  • In some problems, data scientists use Logistic regression to decide what are the important features(Independent variables) that have higher effect on the target(Dependent variable).

    Because logistic regression itself will map variables to variable Importance level. Which may help you deciding which features to use in production. you can search for "logistic regression variable importance" to know more about this.

  • You can also use random forests (Decision trees) and visualize what variables are used in early splits, these variables will have higher effect on dependent variable too. then you can pick n features based on your preference.
  • $\begingroup$ I think this link may help you too analyticsvidhya.com/blog/2016/12/… $\endgroup$ – Amr Tarek Elmzayen Feb 19 '19 at 11:31
  • $\begingroup$ Thanking you very much Ferdi. As u suggested logistic regression (LR) for feature selection, but problem is that LR is binary classifier whereas Ordinal LR is a multiclass classifier. So Feature selected by LR, are suitable enough for Ordinal LR? $\endgroup$ – Abdul Waheed Feb 20 '19 at 8:21
  • $\begingroup$ Actually Abdul, I am the one who answered not Ferdi. Fredi just edited my Answer and removed (Best of luck) I wrote to you at the end. anyway I think Yes you can use these feature at any type of models after you knew that they has an affect on your target (dependent variable) Best Of luck Abdul. $\endgroup$ – Amr Tarek Elmzayen Feb 20 '19 at 12:44
  • $\begingroup$ Jazakallahu Khayr. But how to utilize logistic regression to know the importance of features (independent variables)? can you pls share any R code for it? I am a novice in this field, your help will be appreciated. $\endgroup$ – Abdul Waheed Feb 21 '19 at 4:29
  • $\begingroup$ ` library(caret) mydata <- data.frame(y = c(1,0,0,0,1,1), x1 = c(1,1,0,1,0,0), x2 = c(1,1,1,0,0,1), x3 = c(1,0,1,1,0,0)) fit <- glm(y~x1+x2+x3,data=mydata,family=binomial()) summary(fit) varImp(fit, scale = FALSE) ` referring to link $\endgroup$ – Amr Tarek Elmzayen Feb 21 '19 at 8:55

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