hex.genmodel.easy.prediction

Class MultinomialModelPrediction

java.lang.Object

hex.genmodel.easy.prediction.AbstractPrediction

hex.genmodel.easy.prediction.MultinomialModelPrediction

All Implemented Interfaces:

java.io.Serializable

public class MultinomialModelPrediction
extends AbstractPrediction

Binomial classification model prediction.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
double[]	classProbabilities This array has an element for each class (aka categorical or factor level) in the response column.
java.lang.String	label Label of the predicted level.
int	labelIndex Index number of the predicted class (aka categorical or factor level) in the response column.
int[]	leafNodeAssignmentIds
java.lang.String[]	leafNodeAssignments
double[]	stageProbabilities Staged predictions of tree algorithms (prediction probabilities of trees per iteration).

Constructor Summary

Constructors

Constructor and Description
MultinomialModelPrediction()

Method Summary

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

labelIndex

public int labelIndex

Index number of the predicted class (aka categorical or factor level) in the response column.

label

public java.lang.String label

Label of the predicted level.

classProbabilities

public double[] classProbabilities

This array has an element for each class (aka categorical or factor level) in the response column. The array corresponds to the level names returned by:

 model.getDomainValues(model.getResponseIdx())
 

"Domain" is the internal H2O term for level names. The values in this array may be Double.NaN, which means NA. If they are valid numeric values, then they will sum up to 1.0.

leafNodeAssignments

public java.lang.String[] leafNodeAssignments

leafNodeAssignmentIds

public int[] leafNodeAssignmentIds

stageProbabilities

public double[] stageProbabilities

Staged predictions of tree algorithms (prediction probabilities of trees per iteration). The output structure is for tree Tt and class Cc: Binomial models: [probability T1.C1, probability T2.C1, ..., Tt.C1] where Tt.C1 correspond to the the probability p0 Multinomial models: [probability T1.C1, probability T1.C2, ..., Tt.Cc]

Constructor Detail

MultinomialModelPrediction

public MultinomialModelPrediction()