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

 # XGBoost Tree Booster has a lot of parameters that we can tune
 # (https://github.com/dmlc/xgboost/blob/master/doc/parameter.md)
 
 my $booster = train(data => $train_data, number_of_rounds => 10, params => {
         objective => 'binary:logistic',
         eta => 1.0,
         max_depth => 2,
         silent => 1
     });
 
 # For binay classification predictions are probability confidence scores in [0, 1]
 #  indicating that the label is positive (1 in the first column of agaricus.txt.test)
 my $predictions = $booster->predict(data => $test_data);
 
 say join "\n", @$predictions[0 .. 10];

=head1 DESCRIPTION

Booster objects control training, prediction and evaluation

Work In Progress, the API may change. Comments and suggestions are welcome!

=head1 METHODS

=head2 update

Update one iteration

=head3 Parameters

=over 4

=item iteration

Current iteration number

=item dtrain

Training data (AI::XGBoost::DMatrix)

=back

=head2 boost

Boost one iteration using your own gradient

=head3 Parameters

=over 4

=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

=item data

Data to predict

=back

=head2 set_param

Set booster parameter

=head3 Example

    $booster->set_param('objective', 'binary:logistic');

=head2 set_attr

Set a string attribute

=head2 get_attr

Get a string attribute

=head2 get_score

Get importance of each feature

=head3 Parameters

=over 4

=item importance_type

Type of importance. Valid values:

=over 4

=item weight

Number of times a feature is used to split the data across all trees

=item gain

Average gain of the feature when it is used in trees

=item cover

Average coverage of the feature when it is used in trees

=back