I am doing logistic regression in r and want to give the best features to the model from a list of 200 variables and 25,000 records. A continuous variable(scc) is having 90 % 0’s. Following is the summary.
1st Qu: 0
3rd Qu: 0
The rest 10% which are non-zero ranging from 1 to 130 and have close to 2500 records. Is these variable useful in predicting dependent variable as most of the values are 0 and if not how to test that before passing it to model.
what I did is used conditional box plot to compare the distribution conditioned on whether the dependent variable is 1 or 0 but the boxplot for both looks same.
bplot.xy(data $ dependent,data $ scc)