Package biz.k11i.xgboost.gbm
Interface GradBooster
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Nested Class Summary
Nested Classes Modifier and Type Interface Description static class
GradBooster.Factory
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Method Summary
All Methods Instance Methods Abstract Methods Modifier and Type Method Description void
loadModel(ModelReader reader, boolean with_pbuffer)
Loads model from stream.float[]
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.float
predictSingle(FVec feat, int ntree_limit)
Generates a prediction for given feature vector.void
setNumClass(int num_class)
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Method Detail
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setNumClass
void setNumClass(int num_class)
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loadModel
void loadModel(ModelReader reader, boolean with_pbuffer) throws java.io.IOException
Loads model from stream.- Parameters:
reader
- input streamwith_pbuffer
- whether the incoming data contains pbuffer- Throws:
java.io.IOException
- If an I/O error occurs
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predict
float[] predict(FVec feat, int ntree_limit)
Generates predictions for given feature vector.- Parameters:
feat
- feature vectorntree_limit
- limit the number of trees used in prediction- Returns:
- prediction result
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predictSingle
float 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 vectorntree_limit
- limit the number of trees used in prediction- Returns:
- prediction result
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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 vectorntree_limit
- limit the number of trees used in prediction- Returns:
- predicted leaf indexes
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