Which feature selection technique to pickup(Boruta vs RFE vs step selection) I have data with 103 columns. I would like to understand which algorithm is best for feature selection and what may be the logic to call any feature as best. 

I run below feature selection algorithms and below is the output:

    1) Boruta(given 11 variables as important)
    2) RFE(given 7 variables as important)
    3) Backward Step Selection(5 variables)
    4) Both Step Selection(5 variables)


I not able to decide which one to pick up; with domain knowledge it appears I must pick up results from Boruta (as it is giving most number of variables and all seems important).

However I don't find any concrete reason to pickup the best combination.
There is a tradeoff between selecting features and precision. Fewer features probably have less precision (predictive power).

Select the features that make sense for your problem taking into consideration the trade-off of information vs performance.  The fewer features the model sees the predictive power that it has.