hex.genmodel.easy.prediction

Class RegressionModelPrediction

java.lang.Object

hex.genmodel.easy.prediction.AbstractPrediction

hex.genmodel.easy.prediction.RegressionModelPrediction

All Implemented Interfaces:

java.io.Serializable

Direct Known Subclasses:

KLimeModelPrediction

public class RegressionModelPrediction
extends AbstractPrediction

Regression model prediction.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
float[]	contributions Per-feature prediction contributions (SHAP values).
int[]	leafNodeAssignmentIds
java.lang.String[]	leafNodeAssignments
double[]	stageProbabilities Staged predictions of tree algorithms (prediction probabilities of trees per iteration).
double	value This value may be Double.NaN, which means NA (this will happen with GLM, for example, if one of the input values for a new data point is NA).

Constructor Summary

Constructors

Constructor and Description
RegressionModelPrediction()

Method Summary

Methods inherited from class java.lang.Object

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

Field Detail

value

public double value

This value may be Double.NaN, which means NA (this will happen with GLM, for example, if one of the input values for a new data point is NA).

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]

contributions

public float[] contributions

Per-feature prediction contributions (SHAP values). Size of the returned array is #features + 1 - there is a feature contribution column for each input feature, the last item is the model bias. The sum of the feature contributions and the bias term is equal to the raw prediction of the model. Raw prediction of tree-based model is the sum of the predictions of the individual trees before the inverse link function is applied to get the actual prediction. For Gaussian distribution the sum of the contributions is equal to the model prediction. (Optional) Available only for supported models (GBM, XGBoost).

Constructor Detail

RegressionModelPrediction

public RegressionModelPrediction()