Uses of Class
ml.dmlc.xgboost4j.java.DMatrix

Packages that use DMatrix

Package	Description
ml.dmlc.xgboost4j.java

Uses of DMatrix in ml.dmlc.xgboost4j.java

Methods in ml.dmlc.xgboost4j.java that return DMatrix

Modifier and Type	Method	Description
DMatrix	DMatrix.slice(int[] rowIndex)	Slice the DMatrix and return a new DMatrix that only contains `rowIndex`.

Methods in ml.dmlc.xgboost4j.java with parameters of type DMatrix

Modifier and Type	Method	Description
void	Booster.boost(DMatrix dtrain, float[] grad, float[] hess)	Deprecated.
void	Booster.boost(DMatrix dtrain, int iter, float[] grad, float[] hess)	Update with give grad and hess
static String[]	XGBoost.crossValidation(DMatrix data, Map<String,Object> params, int round, int nfold, String[] metrics, IObjective obj, IEvaluation eval)	Cross-validation with given parameters.
float	IEvaluation.eval(float[][] predicts, DMatrix dmat)	evaluate with predicts and data
String	Booster.evalSet(DMatrix[] evalMatrixs, String[] evalNames, int iter)	Evaluate the Booster model with given dmatrixs.
String	Booster.evalSet(DMatrix[] evalMatrixs, String[] evalNames, int iter, float[] metricsOut)	Evaluate the Booster model with given dmatrixs.
String	Booster.evalSet(DMatrix[] evalMatrixs, String[] evalNames, IEvaluation eval)	Evaluate the Booster model given customized Evaluation class
String	Booster.evalSet(DMatrix[] evalMatrixs, String[] evalNames, IEvaluation eval, float[] metricsOut)
List<float[]>	IObjective.getGradient(float[][] predicts, DMatrix dtrain)	user define objective function, return gradient and second order gradient
float[][]	Booster.predict(DMatrix data)	Make a prediction with test data in a DMatrix format.
float[][]	Booster.predict(DMatrix data, boolean outputMargin)	Make a prediction with test data in a DMatrix format and output margin.
float[][]	Booster.predict(DMatrix data, boolean outputMargin, int treeLimit)	Advanced predict function with all the options.
float[][]	Booster.predictContrib(DMatrix data, int treeLimit)	Output feature contributions toward predictions of given data
float[][]	Booster.predictLeaf(DMatrix data, int treeLimit)	Predict leaf indices given the data
static Booster	XGBoost.train(DMatrix dtrain, Map<String,Object> params, int round, Map<String,DMatrix> watches, float[][] metrics, IObjective obj, IEvaluation eval, int earlyStoppingRound)	Train a booster given parameters.
static Booster	XGBoost.train(DMatrix dtrain, Map<String,Object> params, int round, Map<String,DMatrix> watches, float[][] metrics, IObjective obj, IEvaluation eval, int earlyStoppingRounds, Booster booster)	Train a booster given parameters.
static Booster	XGBoost.train(DMatrix dtrain, Map<String,Object> params, int round, Map<String,DMatrix> watches, IObjective obj, IEvaluation eval)	Train a booster given parameters.
static Booster	XGBoost.trainAndSaveCheckpoint(DMatrix dtrain, Map<String,Object> params, int numRounds, Map<String,DMatrix> watches, float[][] metrics, IObjective obj, IEvaluation eval, int earlyStoppingRounds, Booster booster, int checkpointInterval, String checkpointPath, org.apache.hadoop.fs.FileSystem fs)
void	Booster.update(DMatrix dtrain, int iter)	Update the booster for one iteration.
void	Booster.update(DMatrix dtrain, int iter, IObjective obj)	Update with customize object functon
void	Booster.update(DMatrix dtrain, IObjective obj)	Deprecated.