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=head2 XGBoosterCreate

Create xgboost learner

Parameters:

=over 4

=item dmats 

matrices that are set to be cached

=item len 

length of dmats

=item out 

handle to the result booster

=back

=head2 XGBoosterFree

Free obj in handle

Parameters:

=over 4

=item handle 

handle to be freed

=back

=head2 XGBoosterSetParam

Update the model in one round using dtrain

Parameters:

=over 4

=item handle

handle

=item name

parameter name

=item value

value of parameter

=back

=head2 XGBoosterBoostOneIter

Update the modelo, by directly specify grandient and second order gradient,
this can be used to replace UpdateOneIter, to support customized loss function

Parameters:

=over 4

=item handle

handle

=item dtrain

training data

=item grad

gradient statistics

=item hess

second order gradinet statistics

=item len

length of grad/hess array

=back

=head2 XGBoosterUpdateOneIter

Update the model in one round using dtrain

Parameters:

=over 4

=item handle 

handle

=item iter

current iteration rounds

=item dtrain

training data

=back

=head2 XGBoosterEvalOneIter

=head2 XGBoosterPredict

Make prediction based on dmat

Parameters:

=over 4

=item handle 

handle

=item dmat 

data matrix

=item option_mask 

bit-mask of options taken in prediction, possible values

=over 4

=item

0: normal prediction

=item

1: output margin instead of transformed value