XGBoostParametersV3 (h2o-bindings version 3.18.0.6 API)

java.lang.Object
- water.bindings.pojos.SchemaV3
- - water.bindings.pojos.ModelParametersSchemaV3
  - - water.bindings.pojos.XGBoostParametersV3

public class XGBoostParametersV3
extends ModelParametersSchemaV3

Field Summary

Fields
Modifier and Type	Field and Description
`TreexgboostXGBoostModelXGBoostParametersBackend`	`backend` Backend.
`TreexgboostXGBoostModelXGBoostParametersBooster`	`booster` Booster type
`double`	`colsampleBylevel` (same as col_sample_rate) Column sample rate (from 0.0 to 1.0)
`double`	`colsampleBytree` (same as col_sample_rate_per_tree) Column sample rate per tree (from 0.0 to 1.0)
`double`	`colSampleRate` (same as colsample_bylevel) Column sample rate (from 0.0 to 1.0)
`double`	`colSampleRatePerTree` (same as colsample_bytree) Column sample rate per tree (from 0.0 to 1.0)
`TreexgboostXGBoostModelXGBoostParametersDMatrixType`	`dmatrixType` Type of DMatrix.
`double`	`eta` (same as learn_rate) Learning rate (from 0.0 to 1.0)
`float`	`gamma` (same as min_split_improvement) Minimum relative improvement in squared error reduction for a split to happen
`int`	`gpuId` Which GPU to use.
`TreexgboostXGBoostModelXGBoostParametersGrowPolicy`	`growPolicy` Grow policy - depthwise is standard GBM, lossguide is LightGBM
`double`	`learnRate` (same as eta) Learning rate (from 0.0 to 1.0)
`float`	`maxAbsLeafnodePred` (same as max_delta_step) Maximum absolute value of a leaf node prediction
`int`	`maxBins` For tree_method=hist only: maximum number of bins
`float`	`maxDeltaStep` (same as max_abs_leafnode_pred) Maximum absolute value of a leaf node prediction
`int`	`maxDepth` Maximum tree depth.
`int`	`maxLeaves` For tree_method=hist only: maximum number of leaves
`double`	`minChildWeight` (same as min_rows) Fewest allowed (weighted) observations in a leaf.
`float`	`minDataInLeaf` For tree_method=hist only: the mininum data in a leaf to keep splitting
`double`	`minRows` (same as min_child_weight) Fewest allowed (weighted) observations in a leaf.
`float`	`minSplitImprovement` (same as gamma) Minimum relative improvement in squared error reduction for a split to happen
`float`	`minSumHessianInLeaf` For tree_method=hist only: the mininum sum of hessian in a leaf to keep splitting
`int`	`nEstimators` (same as ntrees) Number of trees.
`TreexgboostXGBoostModelXGBoostParametersDartNormalizeType`	`normalizeType` For booster=dart only: normalize_type
`int`	`ntrees` (same as n_estimators) Number of trees.
`boolean`	`oneDrop` For booster=dart only: one_drop
`boolean`	`quietMode` Enable quiet mode
`float`	`rateDrop` For booster=dart only: rate_drop (0..1)
`float`	`regAlpha` L1 regularization
`float`	`regLambda` L2 regularization
`double`	`sampleRate` (same as subsample) Row sample rate per tree (from 0.0 to 1.0)
`TreexgboostXGBoostModelXGBoostParametersDartSampleType`	`sampleType` For booster=dart only: sample_type
`int`	`scoreTreeInterval` Score the model after every so many trees.
`long`	`seed` Seed for pseudo random number generator (if applicable)
`float`	`skipDrop` For booster=dart only: skip_drop (0..1)
`double`	`subsample` (same as sample_rate) Row sample rate per tree (from 0.0 to 1.0)
`TreexgboostXGBoostModelXGBoostParametersTreeMethod`	`treeMethod` Tree method

Constructor Summary

Constructors
Constructor and Description

XGBoostParametersV3()
Public constructor

Constructors
Constructor and Description
`XGBoostParametersV3()` Public constructor

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type Method and Description

java.lang.String toString()
Return the contents of this object as a JSON String.
- Methods inherited from class java.lang.Object
  clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`java.lang.String`	`toString()` Return the contents of this object as a JSON String.

- Field Detail
  - ntrees
```
public int ntrees
```
    (same as n_estimators) Number of trees.
  - nEstimators
```
public int nEstimators
```
    (same as ntrees) Number of trees.
  - maxDepth
```
public int maxDepth
```
    Maximum tree depth.
  - minRows
```
public double minRows
```
    (same as min_child_weight) Fewest allowed (weighted) observations in a leaf.
  - minChildWeight
```
public double minChildWeight
```
    (same as min_rows) Fewest allowed (weighted) observations in a leaf.
  - learnRate
```
public double learnRate
```
    (same as eta) Learning rate (from 0.0 to 1.0)
  - eta
```
public double eta
```
    (same as learn_rate) Learning rate (from 0.0 to 1.0)
  - sampleRate
```
public double sampleRate
```
    (same as subsample) Row sample rate per tree (from 0.0 to 1.0)
  - subsample
```
public double subsample
```
    (same as sample_rate) Row sample rate per tree (from 0.0 to 1.0)
  - colSampleRate
```
public double colSampleRate
```
    (same as colsample_bylevel) Column sample rate (from 0.0 to 1.0)
  - colsampleBylevel
```
public double colsampleBylevel
```
    (same as col_sample_rate) Column sample rate (from 0.0 to 1.0)
  - colSampleRatePerTree
```
public double colSampleRatePerTree
```
    (same as colsample_bytree) Column sample rate per tree (from 0.0 to 1.0)
  - colsampleBytree
```
public double colsampleBytree
```
    (same as col_sample_rate_per_tree) Column sample rate per tree (from 0.0 to 1.0)
  - maxAbsLeafnodePred
```
public float maxAbsLeafnodePred
```
    (same as max_delta_step) Maximum absolute value of a leaf node prediction
  - maxDeltaStep
```
public float maxDeltaStep
```
    (same as max_abs_leafnode_pred) Maximum absolute value of a leaf node prediction
  - scoreTreeInterval
```
public int scoreTreeInterval
```
    Score the model after every so many trees. Disabled if set to 0.
  - seed
```
public long seed
```
    Seed for pseudo random number generator (if applicable)
  - minSplitImprovement
```
public float minSplitImprovement
```
    (same as gamma) Minimum relative improvement in squared error reduction for a split to happen
  - gamma
```
public float gamma
```
    (same as min_split_improvement) Minimum relative improvement in squared error reduction for a split to happen
  - maxBins
```
public int maxBins
```
    For tree_method=hist only: maximum number of bins
  - maxLeaves
```
public int maxLeaves
```
    For tree_method=hist only: maximum number of leaves
  - minSumHessianInLeaf
```
public float minSumHessianInLeaf
```
    For tree_method=hist only: the mininum sum of hessian in a leaf to keep splitting
  - minDataInLeaf
```
public float minDataInLeaf
```
    For tree_method=hist only: the mininum data in a leaf to keep splitting
  - treeMethod
```
public TreexgboostXGBoostModelXGBoostParametersTreeMethod treeMethod
```
    Tree method
  - growPolicy
```
public TreexgboostXGBoostModelXGBoostParametersGrowPolicy growPolicy
```
    Grow policy - depthwise is standard GBM, lossguide is LightGBM
  - booster
```
public TreexgboostXGBoostModelXGBoostParametersBooster booster
```
    Booster type
  - regLambda
```
public float regLambda
```
    L2 regularization
  - regAlpha
```
public float regAlpha
```
    L1 regularization
  - quietMode
```
public boolean quietMode
```
    Enable quiet mode
  - sampleType
```
public TreexgboostXGBoostModelXGBoostParametersDartSampleType sampleType
```
    For booster=dart only: sample_type
  - normalizeType
```
public TreexgboostXGBoostModelXGBoostParametersDartNormalizeType normalizeType
```
    For booster=dart only: normalize_type
  - rateDrop
```
public float rateDrop
```
    For booster=dart only: rate_drop (0..1)
  - oneDrop
```
public boolean oneDrop
```
    For booster=dart only: one_drop
  - skipDrop
```
public float skipDrop
```
    For booster=dart only: skip_drop (0..1)
  - dmatrixType
```
public TreexgboostXGBoostModelXGBoostParametersDMatrixType dmatrixType
```
    Type of DMatrix. For sparse, NAs and 0 are treated equally.
  - backend
```
public TreexgboostXGBoostModelXGBoostParametersBackend backend
```
    Backend. By default (auto), a GPU is used if available.
  - gpuId
```
public int gpuId
```
    Which GPU to use.
- Constructor Detail
  - XGBoostParametersV3
```
public XGBoostParametersV3()
```
    Public constructor
- Method Detail
  - toString
```
public java.lang.String toString()
```
    Return the contents of this object as a JSON String.
    
    Overrides:
    
    toString in class ModelParametersSchemaV3

Class XGBoostParametersV3

Field Summary

Fields inherited from class water.bindings.pojos.ModelParametersSchemaV3

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

ntrees

nEstimators

maxDepth

minRows

minChildWeight

learnRate

eta

sampleRate

subsample

colSampleRate

colsampleBylevel

colSampleRatePerTree

colsampleBytree

maxAbsLeafnodePred

maxDeltaStep

scoreTreeInterval

seed

minSplitImprovement

gamma

maxBins

maxLeaves

minSumHessianInLeaf

minDataInLeaf

treeMethod

growPolicy

booster

regLambda

regAlpha

quietMode

sampleType

normalizeType

rateDrop

oneDrop

skipDrop

dmatrixType

backend

gpuId

Constructor Detail

XGBoostParametersV3

Method Detail

toString