This is an abstract class which turns boolean categorizers
(categorizers based on algorithms that can just provide yes/no
categorization decisions for a single document and single category)
into multi-valued categorizers. For instance, the decision tree
categorizer AI::Categorizer::Learner::DecisionTree maintains a
decision tree for each category, then uses it to decide whether a
certain document belongs to the given category.
Any class that inherits from this class should implement the following
Used during training to create a category-specific model. The type of
model you create is up to you - it should be returned as a scalar.
Whatever you return will be available to you in the
get_boolean_score() method, so put any information youll need
during categorization in this scalar.
In addition to $self, this method will be passed three arguments.
The first argument is a reference to an array of <B>positiveB> examples,
i.e. documents that belong to the given category. The next argument
is a reference to an array of <B>negativeB> examples, i.e. documents
that do not belong to the given category. The final argument is
the Category object for the given category.
Used during categorization to assign a score for a single document
relative to a single category. The score should be between 0 and 1,
with a score greater than 0.5 indicating membership in the category.
In addition to $self, this method will be passed two arguments.
The first argument is the document to be categorized. The second
argument is the value returned by create_boolean_model() for this