OutputType
- The (output) type for the decision tree. E.g., Integer.public interface PriorWeightedNodeLearner<OutputType>
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.Modifier and Type | Method and Description |
---|---|
void |
configure(java.util.Map<OutputType,java.lang.Double> priors,
java.util.Map<OutputType,java.lang.Integer> trainCounts)
Configure the node learner with prior weights and training counts.
|
void configure(java.util.Map<OutputType,java.lang.Double> priors, java.util.Map<OutputType,java.lang.Integer> trainCounts)
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
counts.trainCounts
- Frequency counts of the possible output values (i.e.,
categories) in the training data. This parameter should
always be specified.