<p>The missing value parameter works as whatever value you provide for 'missing' parameter it treats it as missing value. For example if you provide 0.5 as missing value, then wherever it finds 0.5 in your data it treats it as missing value. Default is NaN. So what XGBoost does is based on the data it defines one of the path as default path. For example based on one parameter say it can go in two directions either left or right, so one of that will be made default based on the data. So whenever one of the missing value comes as input for a parameter, say you defined 0.5 as missing, then whenever 0.5 comes in the data it takes the default path. Initially I thought it imputes the missing value but it does not. It just defines one of the path as default and whenever any missing value come it takes that default path. This is defined in the paper <a href="https://arxiv.org/abs/1603.02754" rel="noreferrer">XGBoost: A Scalable Tree Boosting System</a></p>
How to use missing parameter of XGBRegressor of scikit-learn
<p>I am working on a dataset which contains missing values in certain columns. I am trying to use XGBRegressor of Scikit-Learn wrapper interface for XGBoost. There it provides a parameter called 'missing' in which you can enter float values or otherwise it takes NaN of python as default. So i need help like how can i use this parameter to fill missing values of the columns in my dataset. It will be helpful if one can provide me a simple example as well.</p>