Class XGBoostOptions
java.lang.Object
org.tribuo.classification.xgboost.XGBoostOptions
- All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.config.Options
,ClassificationOptions<XGBoostClassificationTrainer>
public class XGBoostOptions
extends Object
implements ClassificationOptions<XGBoostClassificationTrainer>
CLI options for training an XGBoost classifier.
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Field Summary
FieldsModifier and TypeFieldDescriptionfloat
int
float
float
float
int
float
int
boolean
float
float
Fields inherited from interface com.oracle.labs.mlrg.olcut.config.Options
header
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionConstructs the trainer based on the provided arguments.Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
Methods inherited from interface com.oracle.labs.mlrg.olcut.config.Options
getOptionsDescription
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Field Details
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xgbBoosterType
@Option(longName="xgb-booster-type", usage="Weak learning algorithm.") public XGBoostTrainer.BoosterType xgbBoosterType -
xgbTreeMethod
@Option(longName="xgb-tree-method", usage="Tree building algorithm.") public XGBoostTrainer.TreeMethod xgbTreeMethod -
xgbEnsembleSize
@Option(longName="xgb-ensemble-size", usage="Number of trees in the ensemble.") public int xgbEnsembleSize -
xbgAlpha
@Option(longName="xgb-alpha", usage="L1 regularization term for weights.") public float xbgAlpha -
xgbMinWeight
@Option(longName="xgb-min-weight", usage="Minimum sum of instance weights needed in a leaf (range [0,Infinity]).") public float xgbMinWeight -
xgbMaxDepth
@Option(longName="xgb-max-depth", usage="Max tree depth (range (0,Integer.MAX_VALUE]).") public int xgbMaxDepth -
xgbEta
@Option(longName="xgb-eta", usage="Step size shrinkage parameter (range [0,1]).") public float xgbEta -
xgbSubsampleFeatures
@Option(longName="xgb-subsample-features", usage="Subsample features for each tree (range (0,1]).") public float xgbSubsampleFeatures -
xgbGamma
@Option(longName="xgb-gamma", usage="Minimum loss reduction to make a split (range [0,Infinity]).") public float xgbGamma -
xgbLambda
@Option(longName="xgb-lambda", usage="L2 regularization term for weights.") public float xgbLambda -
xgbQuiet
@Option(longName="xgb-quiet", usage="Deprecated, use xgb-loglevel.") public boolean xgbQuiet -
xgbLogLevel
@Option(longName="xgb-loglevel", usage="Make the XGBoost training procedure quiet.") public XGBoostTrainer.LoggingVerbosity xgbLogLevel -
xgbSubsample
@Option(longName="xgb-subsample", usage="Subsample size for each tree (range (0,1]).") public float xgbSubsample -
xgbNumThreads
@Option(longName="xgb-num-threads", usage="Number of threads to use (range (1, num hw threads)). The default of 0 means use all hw threads.") public int xgbNumThreads
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Constructor Details
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XGBoostOptions
public XGBoostOptions()
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Method Details
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getTrainer
Description copied from interface:ClassificationOptions
Constructs the trainer based on the provided arguments.- Specified by:
getTrainer
in interfaceClassificationOptions<XGBoostClassificationTrainer>
- Returns:
- The trainer.
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