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lib/AI/XGBoost/Booster.pm  view on Meta::CPAN

=item dtrain

Training data (AI::XGBoost::DMatrix)

=item grad

Gradient of your objective function (Reference to an array)

=item hess

Hessian of your objective function, that is, second order gradient (Reference to an array)

=back

=head2 predict

Predict data using the trained model

=head3 Parameters

=over 4

lib/AI/XGBoost/CAPI/RAW.pm  view on Meta::CPAN

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