public interface PriorWeightedNodeLearner<OutputType>
The PriorWeightedNodeLearner interface specifies the
ability to configure prior weights on the learning algorithm that
searches for a decision function inside a decision tree. The
CategorizationTreeLearner checks if the split criterion
supports this interface, and if it does, configures the split
criterion with prior weights and counts.
Classes implementing DeciderLearner or VectorThresholdMaximumGainLearner should consider whether it makes
sense to also implement this class.
Configure the node learner with prior weights and training counts.
If the prior weights are not specified, this method will
configure default priors that match the relative frequencies of
the different categories in the training data. The frequencies
are based on the given category counts from the training data.
priors - Prior weights for each of the possible output values (i.e.,
the categories for the prediction task). If null, the
method will estimate default priors from the training
trainCounts - Frequency counts of the possible output values (i.e.,
categories) in the training data. This parameter should
always be specified.