biz.k11i.xgboost.gbm

Interface GradBooster

All Superinterfaces:

java.io.Serializable

All Known Implementing Classes:

Dart, GBLinear, GBTree

public interface GradBooster
extends java.io.Serializable

Interface of gradient boosting model.

Nested Class Summary

Nested Classes

Modifier and Type	Interface and Description
static class	GradBooster.Factory

Method Summary

Methods

Modifier and Type	Method and Description
void	loadModel(ModelReader reader, boolean with_pbuffer) Loads model from stream.
double[]	predict(FVec feat, int ntree_limit) Generates predictions for given feature vector.
int[]	predictLeaf(FVec feat, int ntree_limit) Predicts the leaf index of each tree.
double	predictSingle(FVec feat, int ntree_limit) Generates a prediction for given feature vector.
void	setNumClass(int num_class)

Method Detail

setNumClass

void setNumClass(int num_class)

loadModel

void loadModel(ModelReader reader,
             boolean with_pbuffer)
               throws java.io.IOException

Loads model from stream.

Parameters:

reader - input stream

with_pbuffer - whether the incoming data contains pbuffer

Throws:

java.io.IOException - If an I/O error occurs

predict

double[] predict(FVec feat,
               int ntree_limit)

Generates predictions for given feature vector.

Parameters:

feat - feature vector

ntree_limit - limit the number of trees used in prediction

Returns:

prediction result

predictSingle

double predictSingle(FVec feat,
                   int ntree_limit)

Generates a prediction for given feature vector.

This method only works when the model outputs single value.

Parameters:

feat - feature vector

ntree_limit - limit the number of trees used in prediction

Returns:

prediction result

predictLeaf

int[] predictLeaf(FVec feat,
                int ntree_limit)

Predicts the leaf index of each tree. This is only valid in gbtree predictor.

Parameters:

feat - feature vector

ntree_limit - limit the number of trees used in prediction

Returns:

predicted leaf indexes