使用Boruta前 ，需要对缺失值进行填充。 

https://www.analyticsvidhya.com/blog/2016/03/select-important-variables-boruta-package/

Variable selection is an important aspect of model building which every analyst must learn. After all, it helps in building predictive models free from correlated variables, biases and unwanted noise.

A lot of novice analysts assume that keeping all (or more) variables will result in the best model as you are not losing any information. Sadly, that is not true!

How many times has it happened that removing a variable from model has increased your model accuracy ?

At least, it has happened to me. Such variables are often found to be correlated and hinder achieving higher model accuracy. Today, we’ll learn one of the ways of how to get rid of such variables in R. I must say, R has an incredible CRAN repository. Out of all packages, one such available package for variable selection is Boruta Package.

特征选择Boruta