org.allenai.nlpstack.parse.poly.decisiontree
OneVersusAll
Related Doc:
package decisiontree
the binary classifier associated with each outcome
All features used by at least one of the binary subclassifiers.
All features used by at least one of the binary subclassifiers.
the binary classifier associated with each outcome
Classifies an feature vector and optionally returns a "justification" for the classification decision.
Classifies an feature vector and optionally returns a "justification" for the classification decision.
feature vector to classify
(predicted outcome, optional justification for the prediction)
Gets the probability distribution over outcomes.
Gets the probability distribution over outcomes.
feature vector to find outcome distribution for
probability distribution of outcomes according to training data
The OneVersusAll implements multi-outcome classification as a set of binary classifiers.
A ProbabilisticClassifier is associated with each outcome. Suppose there are three outcomes: 0, 1, 2. Then the constructor would take a sequence of three classifiers as its argument: [(0,A), (1,B), (2,C)]. To compute the outcome distribution for a new feature vector v, the OneVersusAll would normalize:
[ A.outcomeDistribution(v)(1), B.outcomeDistribution(v)(1), C.outcomeDistribution(v)(1) ]
i.e. the probability of 1 (true) according to binary classifiers A, B, and C.
QUESTION(MH): is this the best way to normalize these, or would it be better to normalize by summing the logs and then re-applying the exponential operation?
the binary classifier associated with each outcome