Definitions of loss function, predictions, gradient, Hessian for binary classification


#1

I am searching for the code chunks with the definitions of

logistic loss
prediction
gradient
Hessian matrix
used by xgboost in its Python implementation available at https://github.com/dmlc/xgboost/tree/master/python-package/xgboost (*)

I can derive the above formulae analytically using the original paper by Chen and Guestrin (available at https://arxiv.org/abs/1603.02754 ), but I need a deep dive into code.

Can somebody point out the location of those definition in (*)? After a quick search I found nothing.


#2

The Python package is only a wrapper. Please refer to the core C++ codebase.


#3

Thank you Philip; could you kindly point me out the location of the aforementioned C++ code?
I found some .h files under

Any suggestion would save me much time: thank you.


#4

You may want to generate Doxygen doc, by running “make doxygen”


#5

Thank you Philip, could you please briefly elaborate your answer? I thank you.