Serialized Form (h2o-genmodel 3.46.0 API)

Package hex.genmodel
- Class hex.genmodel.GenModel extends java.lang.Object implements Serializable
  - Serialized Fields
    - _names
```
java.lang.String[] _names
```
      Column names; last is response for supervised models
    - _domains
```
java.lang.String[][] _domains
```
      Categorical (factor/enum) mappings, per column. Null for non-enum cols. Columns match the post-init cleanup columns. The last column holds the response col enums for SupervisedModels.
    - _responseColumn
```
java.lang.String _responseColumn
```
      Name of the response column used for training (only for supervised models).
    - _offsetColumn
```
java.lang.String _offsetColumn
```
      Name of the column with offsets (used for certain types of models).
    - _foldColumn
```
java.lang.String _foldColumn
```
    - _treatmentColumn
```
java.lang.String _treatmentColumn
```
      Name of the column determine treatment group, currently only for UpliftDRF models
- Class hex.genmodel.GenMunger extends java.lang.Object implements Serializable
  - Serialized Fields
    - _steps
```
GenMunger.Step<T>[] _steps
```
- Class hex.genmodel.GenMunger.Step extends java.lang.Object implements Serializable
  - Serialized Fields
    - _names
```
java.lang.String[] _names
```
    - _types
```
java.lang.String[] _types
```
    - _outNames
```
java.lang.String[] _outNames
```
    - _params
```
java.util.HashMap<K,V> _params
```
- Class hex.genmodel.MojoModel extends GenModel implements Serializable
  - Serialized Fields
    - _algoName
```
java.lang.String _algoName
```
    - _h2oVersion
```
java.lang.String _h2oVersion
```
    - _category
```
ModelCategory _category
```
    - _uuid
```
java.lang.String _uuid
```
    - _supervised
```
boolean _supervised
```
    - _nfeatures
```
int _nfeatures
```
    - _nclasses
```
int _nclasses
```
    - _balanceClasses
```
boolean _balanceClasses
```
    - _defaultThreshold
```
double _defaultThreshold
```
    - _priorClassDistrib
```
double[] _priorClassDistrib
```
    - _modelClassDistrib
```
double[] _modelClassDistrib
```
    - _mojo_version
```
double _mojo_version
```
    - _modelDescriptor
```
ModelDescriptor _modelDescriptor
```
    - _modelAttributes
```
ModelAttributes _modelAttributes
```
    - _reproducibilityInformation
```
Table[] _reproducibilityInformation
```

Package hex.genmodel.algos.coxph

Class hex.genmodel.algos.coxph.CoxPHMojoModel extends MojoModel implements Serializable

Serialized Fields

_coef
```
double[] _coef
```
_strata
```
java.util.HashMap<K,V> _strata
```
_strata_len
```
int _strata_len
```
_x_mean_cat
```
double[][] _x_mean_cat
```
_x_mean_num
```
double[][] _x_mean_num
```
_cat_offsets
```
int[] _cat_offsets
```
_cats
```
int _cats
```
_lpBase
```
double[] _lpBase
```
_useAllFactorLevels
```
boolean _useAllFactorLevels
```
_nums
```
int _nums
```
_interactions_1
```
int[] _interactions_1
```
_interactions_2
```
int[] _interactions_2
```
_interaction_targets
```
int[] _interaction_targets
```
_is_enum_1
```
boolean[] _is_enum_1
```

_interaction_column_index

java.util.HashSet<E> _interaction_column_index

_interaction_column_domains

java.util.HashMap<K,V> _interaction_column_domains

_interaction_types

hex.genmodel.algos.coxph.CoxPHMojoModel.InteractionTypes[] _interaction_types

_num_offsets
```
int[] _num_offsets
```

Package hex.genmodel.algos.deeplearning
- Class hex.genmodel.algos.deeplearning.ActivationUtils.ExpRectifierDropoutOut extends ActivationUtils.ExpRectifierOut implements Serializable
- Class hex.genmodel.algos.deeplearning.ActivationUtils.ExpRectifierOut extends java.lang.Object implements Serializable
- Class hex.genmodel.algos.deeplearning.ActivationUtils.LinearOut extends java.lang.Object implements Serializable
- Class hex.genmodel.algos.deeplearning.ActivationUtils.MaxoutDropoutOut extends ActivationUtils.MaxoutOut implements Serializable
- Class hex.genmodel.algos.deeplearning.ActivationUtils.MaxoutOut extends java.lang.Object implements Serializable
- Class hex.genmodel.algos.deeplearning.ActivationUtils.RectifierDropoutOut extends ActivationUtils.RectifierOut implements Serializable
- Class hex.genmodel.algos.deeplearning.ActivationUtils.RectifierOut extends java.lang.Object implements Serializable
- Class hex.genmodel.algos.deeplearning.ActivationUtils.SoftmaxOut extends java.lang.Object implements Serializable
- Class hex.genmodel.algos.deeplearning.ActivationUtils.TanhDropoutOut extends ActivationUtils.TanhOut implements Serializable
- Class hex.genmodel.algos.deeplearning.ActivationUtils.TanhOut extends java.lang.Object implements Serializable
- Class hex.genmodel.algos.deeplearning.DeeplearningMojoModel extends MojoModel implements Serializable
  - Serialized Fields
    - _mini_batch_size
```
int _mini_batch_size
```
    - _nums
```
int _nums
```
    - _cats
```
int _cats
```
    - _catoffsets
```
int[] _catoffsets
```
    - _normmul
```
double[] _normmul
```
    - _normsub
```
double[] _normsub
```
    - _normrespmul
```
double[] _normrespmul
```
    - _normrespsub
```
double[] _normrespsub
```
    - _use_all_factor_levels
```
boolean _use_all_factor_levels
```
    - _activation
```
java.lang.String _activation
```
    - _allActivations
```
java.lang.String[] _allActivations
```
    - _imputeMeans
```
boolean _imputeMeans
```
    - _units
```
int[] _units
```
    - _all_drop_out_ratios
```
double[] _all_drop_out_ratios
```
    - _weightsAndBias
```
DeeplearningMojoModel.StoreWeightsBias[] _weightsAndBias
```
    - _catNAFill
```
int[] _catNAFill
```
    - _numLayers
```
int _numLayers
```
    - _family
```
DistributionFamily _family
```
    - _genmodel_encoding
```
java.lang.String _genmodel_encoding
```
    - _orig_names
```
java.lang.String[] _orig_names
```
    - _orig_domain_values
```
java.lang.String[][] _orig_domain_values
```
    - _orig_projection_array
```
double[] _orig_projection_array
```
- Class hex.genmodel.algos.deeplearning.DeeplearningMojoModel.StoreWeightsBias extends java.lang.Object implements Serializable
  - Serialized Fields
    - _wValues
```
float[] _wValues
```
    - _bValues
```
double[] _bValues
```
Package hex.genmodel.algos.drf
- Class hex.genmodel.algos.drf.DrfMojoModel extends SharedTreeMojoModelWithContributions implements Serializable
  - Serialized Fields
    - _binomial_double_trees
```
boolean _binomial_double_trees
```
Package hex.genmodel.algos.ensemble
- Class hex.genmodel.algos.ensemble.StackedEnsembleMojoModel extends MojoModel implements Serializable
  - Serialized Fields
    - _metaLearner
```
MojoModel _metaLearner
```
    - _useLogitMetaLearnerTransform
```
boolean _useLogitMetaLearnerTransform
```
    - _baseModels
```
hex.genmodel.algos.ensemble.StackedEnsembleMojoModel.StackedEnsembleMojoSubModel[] _baseModels
```
    - _baseModelNum
```
int _baseModelNum
```
Package hex.genmodel.algos.gam
- Class hex.genmodel.algos.gam.GamMojoModel extends GamMojoModelBase implements Serializable
  - Serialized Fields
    - _classifier
```
boolean _classifier
```
- Class hex.genmodel.algos.gam.GamMojoModelBase extends MojoModel implements Serializable
  - Serialized Fields
    - _link_function
```
LinkFunctionType _link_function
```
    - _useAllFactorLevels
```
boolean _useAllFactorLevels
```
    - _cats
```
int _cats
```
    - _catNAFills
```
int[] _catNAFills
```
    - _catOffsets
```
int[] _catOffsets
```
    - _nums
```
int _nums
```
    - _numsCenter
```
int _numsCenter
```
    - _numNAFillsCenter
```
double[] _numNAFillsCenter
```
    - _meanImputation
```
boolean _meanImputation
```
    - _beta_no_center
```
double[] _beta_no_center
```
    - _beta_center
```
double[] _beta_center
```
    - _beta_multinomial
```
double[][] _beta_multinomial
```
    - _beta_multinomial_no_center
```
double[][] _beta_multinomial_no_center
```
    - _beta_multinomial_center
```
double[][] _beta_multinomial_center
```
    - _spline_orders
```
int[] _spline_orders
```
    - _spline_orders_sorted
```
int[] _spline_orders_sorted
```
    - _family
```
DistributionFamily _family
```
    - _gam_columns
```
java.lang.String[][] _gam_columns
```
    - _gam_columns_sorted
```
java.lang.String[][] _gam_columns_sorted
```
    - _d
```
int[] _d
```
    - _m
```
int[] _m
```
    - _M
```
int[] _M
```
    - _gamPredSize
```
int[] _gamPredSize
```
    - _num_gam_columns
```
int _num_gam_columns
```
    - _bs
```
int[] _bs
```
    - _bs_sorted
```
int[] _bs_sorted
```
    - _num_knots
```
int[] _num_knots
```
    - _num_knots_sorted
```
int[] _num_knots_sorted
```
    - _num_knots_sorted_minus1
```
int[] _num_knots_sorted_minus1
```
    - _numBasisSize
```
int[] _numBasisSize
```
    - _numMSBasisSize
```
int[] _numMSBasisSize
```
    - _num_knots_TP
```
int[] _num_knots_TP
```
    - _knots
```
double[][][] _knots
```
    - _binvD
```
double[][][] _binvD
```
    - _zTranspose
```
double[][][] _zTranspose
```
    - _zTransposeCS
```
double[][][] _zTransposeCS
```
    - _gamColNames
```
java.lang.String[][] _gamColNames
```
    - _gamColNamesCenter
```
java.lang.String[][] _gamColNamesCenter
```
    - _names_no_centering
```
java.lang.String[] _names_no_centering
```
    - _totFeatureSize
```
int _totFeatureSize
```
    - _betaSizePerClass
```
int _betaSizePerClass
```
    - _betaCenterSizePerClass
```
int _betaCenterSizePerClass
```
    - _tweedieLinkPower
```
double _tweedieLinkPower
```
    - _hj
```
double[][] _hj
```
    - _numExpandedGamCols
```
int _numExpandedGamCols
```
    - _numExpandedGamColsCenter
```
int _numExpandedGamColsCenter
```
    - _lastClass
```
int _lastClass
```
    - _allPolyBasisList
```
int[][][] _allPolyBasisList
```
    - _numTPCol
```
int _numTPCol
```
    - _numCSCol
```
int _numCSCol
```
    - _numISCol
```
int _numISCol
```
    - _numMSCol
```
int _numMSCol
```
    - _tpDistzCSSize
```
int[] _tpDistzCSSize
```
    - _dEven
```
boolean[] _dEven
```
    - _constantTerms
```
double[] _constantTerms
```
    - _gamColMeansRaw
```
double[][] _gamColMeansRaw
```
    - _oneOGamColStd
```
double[][] _oneOGamColStd
```
    - _standardize
```
boolean _standardize
```
    - _iSplineBasis
```
ISplines[] _iSplineBasis
```
    - _mSplineBasis
```
MSplines[] _mSplineBasis
```
- Class hex.genmodel.algos.gam.GamMojoMultinomialModel extends GamMojoModelBase implements Serializable
  - Serialized Fields
    - _trueMultinomial
```
boolean _trueMultinomial
```
- Class hex.genmodel.algos.gam.GamRowToRawDataConverter extends RowToRawDataConverter implements Serializable
  - Serialized Fields
    - _m
```
GamMojoModelBase _m
```
- Class hex.genmodel.algos.gam.ISplines extends java.lang.Object implements Serializable
  - Serialized Fields
    - _knotsWDuplicates
```
double[] _knotsWDuplicates
```
    - _order
```
int _order
```
    - _numIBasis
```
int _numIBasis
```
    - _bSplines
```
NBSplinesTypeII _bSplines
```
    - _iSplines
```
hex.genmodel.algos.gam.ISplines.ISplineBasis[] _iSplines
```
- Class hex.genmodel.algos.gam.MSplines extends java.lang.Object implements Serializable
  - Serialized Fields
    - _knotsWDuplicates
```
double[] _knotsWDuplicates
```
    - _order
```
int _order
```
    - _numMBasis
```
int _numMBasis
```
    - _mSplines
```
NBSplinesTypeI.MSplineBasis[] _mSplines
```
- Class hex.genmodel.algos.gam.NBSplinesTypeI extends java.lang.Object implements Serializable
  - Serialized Fields
    - _order
```
int _order
```
    - _nodeCoeffs
```
double[][] _nodeCoeffs
```
    - _coeffLeft
```
double[] _coeffLeft
```
    - _coeffRight
```
double[] _coeffRight
```
    - _knots
```
double[] _knots
```
    - _commonConst
```
double _commonConst
```
    - _left
```
NBSplinesTypeI _left
```
    - _right
```
NBSplinesTypeI _right
```
    - _totBasisFuncs
```
int _totBasisFuncs
```
    - _nKnots
```
int _nKnots
```
- Class hex.genmodel.algos.gam.NBSplinesTypeI.MSplineBasis extends java.lang.Object implements Serializable
  - Serialized Fields
    - _knots
```
double[] _knots
```
    - _numerator
```
double[] _numerator
```
    - _oneOverDenominator
```
double[] _oneOverDenominator
```
    - _first
```
NBSplinesTypeI.MSplineBasis _first
```
    - _second
```
NBSplinesTypeI.MSplineBasis _second
```
    - _constant
```
double _constant
```
- Class hex.genmodel.algos.gam.NBSplinesTypeII extends java.lang.Object implements Serializable
  - Serialized Fields
    - _order
```
int _order
```
    - _nKnots
```
int _nKnots
```
    - _knots
```
double[] _knots
```
    - _totBasisFuncs
```
int _totBasisFuncs
```
    - _basisFuncs
```
NBSplinesTypeII.BSplineBasis[] _basisFuncs
```
- Class hex.genmodel.algos.gam.NBSplinesTypeII.BSplineBasis extends java.lang.Object implements Serializable
  - Serialized Fields
    - _knots
```
double[] _knots
```
    - _numerator
```
double[] _numerator
```
    - _oneOverDenominator
```
double[] _oneOverDenominator
```
    - _first
```
NBSplinesTypeII.BSplineBasis _first
```
    - _second
```
NBSplinesTypeII.BSplineBasis _second
```
Package hex.genmodel.algos.gbm
- Class hex.genmodel.algos.gbm.GbmMojoModel extends SharedTreeMojoModelWithContributions implements Serializable
  - Serialized Fields
    - _family
```
DistributionFamily _family
```
    - _link_function
```
LinkFunctionType _link_function
```
    - _init_f
```
double _init_f
```
Package hex.genmodel.algos.glm
- Class hex.genmodel.algos.glm.GlmMojoModel extends GlmMojoModelBase implements Serializable
  - Serialized Fields
    - _link
```
java.lang.String _link
```
    - _tweedieLinkPower
```
double _tweedieLinkPower
```
    - _linkFn
```
hex.genmodel.algos.glm.GlmMojoModel.Function1 _linkFn
```
    - _binomial
```
boolean _binomial
```
- Class hex.genmodel.algos.glm.GlmMojoModelBase extends MojoModel implements Serializable
  - Serialized Fields
    - _useAllFactorLevels
```
boolean _useAllFactorLevels
```
    - _cats
```
int _cats
```
    - _catModes
```
int[] _catModes
```
    - _catOffsets
```
int[] _catOffsets
```
    - _nums
```
int _nums
```
    - _numMeans
```
double[] _numMeans
```
    - _meanImputation
```
boolean _meanImputation
```
    - _beta
```
double[] _beta
```
    - _family
```
java.lang.String _family
```
    - _versionSupportOffset
```
boolean _versionSupportOffset
```
    - _dispersion_estimated
```
double _dispersion_estimated
```
- Class hex.genmodel.algos.glm.GlmMultinomialMojoModel extends GlmMojoModelBase implements Serializable
  - Serialized Fields
    - P
```
int P
```
    - noff
```
int noff
```
- Class hex.genmodel.algos.glm.GlmOrdinalMojoModel extends GlmMojoModelBase implements Serializable
  - Serialized Fields
    - P
```
int P
```
    - noff
```
int noff
```
    - lastClass
```
int lastClass
```
    - icptIndices
```
int[] icptIndices
```

Package hex.genmodel.algos.glrm

Class hex.genmodel.algos.glrm.GlrmMojoModel extends MojoModel implements Serializable

Serialized Fields

_ncolA
```
int _ncolA
```
_ncolX
```
int _ncolX
```
_ncolY
```
int _ncolY
```
_nrowY
```
int _nrowY
```
_archetypes
```
double[][] _archetypes
```
_archetypes_raw
```
double[][] _archetypes_raw
```
_numLevels
```
int[] _numLevels
```
_catOffsets
```
int[] _catOffsets
```
_permutation
```
int[] _permutation
```
_losses
```
GlrmLoss[] _losses
```
_regx
```
GlrmRegularizer _regx
```
_gammax
```
double _gammax
```
_init
```
GlrmInitialization _init
```
_ncats
```
int _ncats
```
_nnums
```
int _nnums
```
_normSub
```
double[] _normSub
```
_normMul
```
double[] _normMul
```
_seed
```
long _seed
```
_transposed
```
boolean _transposed
```
_reverse_transform
```
boolean _reverse_transform
```
_accuracyEps
```
double _accuracyEps
```
_iterNumber
```
int _iterNumber
```
_rcnt
```
long _rcnt
```
_numAlphaFactors
```
int _numAlphaFactors
```
_allAlphas
```
double[] _allAlphas
```

Package hex.genmodel.algos.isofor
- Class hex.genmodel.algos.isofor.IsolationForestMojoModel extends SharedTreeMojoModel implements Serializable
  - Serialized Fields
    - _min_path_length
```
int _min_path_length
```
    - _max_path_length
```
int _max_path_length
```
    - _outputAnomalyFlag
```
boolean _outputAnomalyFlag
```
Package hex.genmodel.algos.isoforextended
- Class hex.genmodel.algos.isoforextended.ExtendedIsolationForestMojoModel extends MojoModel implements Serializable
  - Serialized Fields
    - _ntrees
```
int _ntrees
```
    - _sample_size
```
long _sample_size
```
    - _compressedTrees
```
byte[][] _compressedTrees
```
    - _scoreIsolationTree
```
ScoreIsolationTree _scoreIsolationTree
```
Package hex.genmodel.algos.isotonic
- Class hex.genmodel.algos.isotonic.IsotonicCalibrator extends java.lang.Object implements Serializable
  - Serialized Fields
    - _min_x
```
double _min_x
```
    - _max_x
```
double _max_x
```
    - _thresholds_x
```
double[] _thresholds_x
```
    - _thresholds_y
```
double[] _thresholds_y
```
- Class hex.genmodel.algos.isotonic.IsotonicRegressionMojoModel extends MojoModel implements Serializable
  - Serialized Fields
    - _isotonic_calibrator
```
IsotonicCalibrator _isotonic_calibrator
```
Package hex.genmodel.algos.klime
- Class hex.genmodel.algos.klime.KLimeMojoModel extends MojoModel implements Serializable
  - Serialized Fields
    - _clusteringModel
```
MojoModel _clusteringModel
```
    - _globalRegressionModel
```
MojoModel _globalRegressionModel
```
    - _clusterRegressionModels
```
MojoModel[] _clusterRegressionModels
```
    - _rowSubsetMap
```
int[] _rowSubsetMap
```
Package hex.genmodel.algos.kmeans
- Class hex.genmodel.algos.kmeans.KMeansMojoModel extends MojoModel implements Serializable
  - Serialized Fields
    - _standardize
```
boolean _standardize
```
    - _centers
```
double[][] _centers
```
    - _means
```
double[] _means
```
    - _mults
```
double[] _mults
```
    - _modes
```
int[] _modes
```
Package hex.genmodel.algos.pca
- Class hex.genmodel.algos.pca.PCAMojoModel extends MojoModel implements Serializable
  - Serialized Fields
    - _eigenvectors_raw
```
double[][] _eigenvectors_raw
```
    - _catOffsets
```
int[] _catOffsets
```
    - _permutation
```
int[] _permutation
```
    - _ncats
```
int _ncats
```
    - _nnums
```
int _nnums
```
    - _normSub
```
double[] _normSub
```
    - _normMul
```
double[] _normMul
```
    - _use_all_factor_levels
```
boolean _use_all_factor_levels
```
    - _pca_method
```
java.lang.String _pca_method
```
    - _pca_impl
```
java.lang.String _pca_impl
```
    - _k
```
int _k
```
    - _eigenVectorSize
```
int _eigenVectorSize
```
Package hex.genmodel.algos.pipeline
- Class hex.genmodel.algos.pipeline.MojoPipeline extends MojoModel implements Serializable
  - Serialized Fields
    - _mainModel
```
MojoModel _mainModel
```
    - _sourceRowIndices
```
int[] _sourceRowIndices
```
    - _targetMainModelRowIndices
```
int[] _targetMainModelRowIndices
```
    - _generatedColumnCount
```
int _generatedColumnCount
```
    - _models
```
hex.genmodel.algos.pipeline.MojoPipeline.PipelineSubModel[] _models
```
Package hex.genmodel.algos.psvm
- Class hex.genmodel.algos.psvm.KernelParameters extends java.lang.Object implements Serializable
  - Serialized Fields
    - _gamma
```
double _gamma
```
Package hex.genmodel.algos.rulefit
- Class hex.genmodel.algos.rulefit.MojoCondition extends java.lang.Object implements Serializable
  - Serialized Fields
    - _featureIndex
```
int _featureIndex
```
    - _type
```
MojoCondition.Type _type
```
    - _operator
```
MojoCondition.Operator _operator
```
    - _featureName
```
java.lang.String _featureName
```
    - _NAsIncluded
```
boolean _NAsIncluded
```
    - _languageCondition
```
java.lang.String _languageCondition
```
    - _numThreshold
```
double _numThreshold
```
    - _languageCatThreshold
```
java.lang.String[] _languageCatThreshold
```
    - _catThreshold
```
int[] _catThreshold
```
- Class hex.genmodel.algos.rulefit.MojoRule extends java.lang.Object implements Serializable
  - Serialized Fields
    - _conditions
```
MojoCondition[] _conditions
```
    - _predictionValue
```
double _predictionValue
```
    - _languageRule
```
java.lang.String _languageRule
```
    - _coefficient
```
double _coefficient
```
    - _varName
```
java.lang.String _varName
```
    - _support
```
double _support
```
- Class hex.genmodel.algos.rulefit.MojoRuleEnsemble extends java.lang.Object implements Serializable
  - Serialized Fields
    - _orderedRules
```
MojoRule[][][] _orderedRules
```
- Class hex.genmodel.algos.rulefit.RuleFitMojoModel extends MojoModel implements Serializable
  - Serialized Fields
    - _linearModel
```
MojoModel _linearModel
```
    - _ruleEnsemble
```
MojoRuleEnsemble _ruleEnsemble
```
    - _depth
```
int _depth
```
    - _ntrees
```
int _ntrees
```
    - _modelType
```
RuleFitMojoModel.ModelType _modelType
```
    - _dataFromRulesCodes
```
java.lang.String[] _dataFromRulesCodes
```
    - _weightsColumn
```
java.lang.String _weightsColumn
```
    - _linearNames
```
java.lang.String[] _linearNames
```
Package hex.genmodel.algos.svm
- Class hex.genmodel.algos.svm.SvmMojoModel extends MojoModel implements Serializable
  - Serialized Fields
    - meanImputation
```
boolean meanImputation
```
    - weights
```
double[] weights
```
    - means
```
double[] means
```
    - interceptor
```
double interceptor
```
    - defaultThreshold
```
double defaultThreshold
```
    - threshold
```
double threshold
```
Package hex.genmodel.algos.targetencoder
- Class hex.genmodel.algos.targetencoder.ColumnsMapping extends java.lang.Object implements Serializable
  - Serialized Fields
    - _from
```
java.lang.String[] _from
```
    - _to
```
java.lang.String[] _to
```
- Class hex.genmodel.algos.targetencoder.EncodingMap extends java.lang.Object implements Serializable
  - Serialized Fields
    - _encodingMap
```
java.util.Map<K,V> _encodingMap
```
      Represents mapping from categorical level index to: - a 2-elements array of `numerator` and `denominator` for regression and binary problems. - a 3-elements array of `numerator`, `denominator` and `targetclass` for multiclass problems. Those are then used to calculate the target frequencies. Note that the last (group of) index is reserved for NA level and we rely on this fact. Example: a binary mapping (regression is similar with numerator accepting any nunerical value): Map ( 0 = "A" -> [ 4, 7 ], 1 = "B" -> [ 2, 8 ], 2 = "C" -> [ 7, 12 ], 3 = "COL_NAME_NA" -> [ 5, 6 ], ) a multiclass ('y' = 0, 'n' = 1, 'maybe' = 2, 'NA' = 3) mapping: Map ( 0 = "A" -> Map ( "y" = 0 -> [ 4, 7 ], "n" = 1 -> [ 2, 7 ], "maybe" = 2 -> [ 1, 7 ] "NA" = 3 -> [ 0, 7 ] ), 1 = "B" -> Map ( "y" = 0 -> [ 2, 8 ], "n" = 1 -> [ 3, 8 ], "maybe" = 2 -> [ 3, 8 ] "NA" = 3 -> [ 0, 8 ] ), ... )
    - priors
```
java.util.Map<K,V> priors
```
    - _nclasses
```
int _nclasses
```
- Class hex.genmodel.algos.targetencoder.EncodingMaps extends java.lang.Object implements Serializable
  - Serialized Fields
    - _encodingMaps
```
java.util.Map<K,V> _encodingMaps
```
      Outer Map stores encoding maps for each categorical column. Example: Map( "categorical_col_name_1" -> EncodingMap ( ... ) Map( "categorical_col_name_2" -> EncodingMap ( ... )
- Class hex.genmodel.algos.targetencoder.TargetEncoderMojoModel extends MojoModel implements Serializable
  - Serialized Fields
    - _columnNameToIdx
```
java.util.Map<K,V> _columnNameToIdx
```
    - _teColumn2HasNAs
```
java.util.Map<K,V> _teColumn2HasNAs
```
    - _withBlending
```
boolean _withBlending
```
    - _inflectionPoint
```
double _inflectionPoint
```
    - _smoothing
```
double _smoothing
```
    - _inencMapping
```
java.util.List<E> _inencMapping
```
    - _inoutMapping
```
java.util.List<E> _inoutMapping
```
    - _nonPredictors
```
java.util.List<E> _nonPredictors
```
    - _encodingsByCol
```
java.util.Map<K,V> _encodingsByCol
```
    - _keepOriginalCategoricalColumns
```
boolean _keepOriginalCategoricalColumns
```
    - _imputeUnknownLevels
```
boolean _imputeUnknownLevels
```
      Whether during training of the model unknown categorical level was imputed with NA level. It will determine whether we are using posterior probability of NA level or prior probability when substitution didn't take place. TODO Default value is hardcoded to `true` as we need to investigate PUBDEV-6704
Package hex.genmodel.algos.tree
- Class hex.genmodel.algos.tree.ContributionsPredictor extends java.lang.Object implements Serializable
  - Serialized Fields
    - _ncontribs
```
int _ncontribs
```
    - _contribution_names
```
java.lang.String[] _contribution_names
```
    - _treeSHAPPredictor
```
TreeSHAPPredictor<R> _treeSHAPPredictor
```
    - _workspaceSize
```
int _workspaceSize
```
- Class hex.genmodel.algos.tree.ScoreIsolationTree0 extends java.lang.Object implements Serializable
- Class hex.genmodel.algos.tree.ScoreTree0 extends java.lang.Object implements Serializable
- Class hex.genmodel.algos.tree.ScoreTree1 extends java.lang.Object implements Serializable
- Class hex.genmodel.algos.tree.ScoreTree2 extends java.lang.Object implements Serializable
- Class hex.genmodel.algos.tree.SharedTreeMojoModel extends MojoModel implements Serializable
  - Serialized Fields
    - _scoreTree
```
ScoreTree _scoreTree
```
    - _ntree_groups
```
int _ntree_groups
```
      _ntree_groups is the number of trees requested by the user. For binomial case or regression this is also the total number of trees trained; however in multinomial case each requested "tree" is actually represented as a group of trees, with _ntrees_per_group trees in each group. Each of these individual trees assesses the likelihood that a given observation belongs to class A, B, C, etc. of a multiclass response.
    - _ntrees_per_group
```
int _ntrees_per_group
```
    - _compressed_trees
```
byte[][] _compressed_trees
```
      Array of binary tree data, each tree being a byte[] array. The trees are logically grouped into a rectangular grid of dimensions SharedTreeMojoModel._ntree_groups x SharedTreeMojoModel._ntrees_per_group, however physically they are stored as 1-dimensional list, and an [i, j] logical tree is mapped to the index SharedTreeMojoModel.treeIndex(int, int).
    - _compressed_trees_aux
```
byte[][] _compressed_trees_aux
```
      Array of auxiliary binary tree data, each being a byte[] array.
    - _calib_glm_beta
```
double[] _calib_glm_beta
```
      GLM's beta used for calibrating output probabilities using Platt Scaling.
    - _isotonic_calibrator
```
IsotonicCalibrator _isotonic_calibrator
```
      For calibrating using Isotonic Regression
    - _genmodel_encoding
```
java.lang.String _genmodel_encoding
```
    - _orig_names
```
java.lang.String[] _orig_names
```
    - _orig_domain_values
```
java.lang.String[][] _orig_domain_values
```
    - _orig_projection_array
```
double[] _orig_projection_array
```
- Class hex.genmodel.algos.tree.SharedTreeMojoModelWithContributions extends SharedTreeMojoModel implements Serializable
- Class hex.genmodel.algos.tree.SharedTreeMojoModelWithContributions.SharedTreeContributionsPredictor extends ContributionsPredictor<double[]> implements Serializable
- Class hex.genmodel.algos.tree.TreeSHAP extends java.lang.Object implements Serializable
  - Serialized Fields
    - rootNodeId
```
int rootNodeId
```
    - nodes
```
ai.h2o.algos.tree.INode<T>[] nodes
```
    - stats
```
ai.h2o.algos.tree.INodeStat[] stats
```
    - expectedTreeValue
```
float expectedTreeValue
```
- Class hex.genmodel.algos.tree.TreeSHAPEnsemble extends java.lang.Object implements Serializable
  - Serialized Fields
    - _predictors
```
TreeSHAPPredictor<R>[] _predictors
```
    - _initPred
```
float _initPred
```
    - _wsMakerIndex
```
int _wsMakerIndex
```
Package hex.genmodel.algos.upliftdrf
- Class hex.genmodel.algos.upliftdrf.UpliftDrfMojoModel extends SharedTreeMojoModel implements Serializable
  - Serialized Fields
    - _thresholds
```
double[] _thresholds
```
Package hex.genmodel.algos.word2vec
- Class hex.genmodel.algos.word2vec.Word2VecMojoModel extends MojoModel implements Serializable
  - Serialized Fields
    - _vecSize
```
int _vecSize
```
    - _embeddings
```
java.util.HashMap<K,V> _embeddings
```
Package hex.genmodel.attributes
- Class hex.genmodel.attributes.DeepLearningModelAttributes extends ModelAttributes implements Serializable
  - Serialized Fields
    - _variableImportances
```
VariableImportances _variableImportances
```
- Class hex.genmodel.attributes.ModelAttributes extends java.lang.Object implements Serializable
  - Serialized Fields
    - _modelSummary
```
Table _modelSummary
```
    - _scoring_history
```
Table _scoring_history
```
    - _trainingMetrics
```
MojoModelMetrics _trainingMetrics
```
    - _validation_metrics
```
MojoModelMetrics _validation_metrics
```
    - _cross_validation_metrics
```
MojoModelMetrics _cross_validation_metrics
```
    - _cross_validation_metrics_summary
```
Table _cross_validation_metrics_summary
```
    - _model_parameters
```
ModelParameter[] _model_parameters
```
- Class hex.genmodel.attributes.ModelAttributesGLM extends ModelAttributes implements Serializable
  - Serialized Fields
    - _coefficients_table
```
Table _coefficients_table
```
    - _variableImportances
```
VariableImportances _variableImportances
```
- Class hex.genmodel.attributes.SharedTreeModelAttributes extends ModelAttributes implements Serializable
  - Serialized Fields
    - _variableImportances
```
VariableImportances _variableImportances
```
- Class hex.genmodel.attributes.Table extends java.lang.Object implements Serializable
  - Serialized Fields
    - _tableHeader
```
java.lang.String _tableHeader
```
    - _tableDescription
```
java.lang.String _tableDescription
```
    - _rowHeaders
```
java.lang.String[] _rowHeaders
```
    - _colHeaders
```
java.lang.String[] _colHeaders
```
    - _colTypes
```
Table.ColumnType[] _colTypes
```
    - _cellValues
```
java.lang.Object[][] _cellValues
```
    - _colHeaderForRowHeaders
```
java.lang.String _colHeaderForRowHeaders
```
    - _colFormats
```
java.lang.String[] _colFormats
```
- Class hex.genmodel.attributes.VariableImportances extends java.lang.Object implements Serializable
  - Serialized Fields
    - _variables
```
java.lang.String[] _variables
```
    - _importances
```
double[] _importances
```
Package hex.genmodel.attributes.metrics
- Class hex.genmodel.attributes.metrics.MojoModelMetrics extends java.lang.Object implements Serializable
  - Serialized Fields
    - _frame_checksum
```
long _frame_checksum
```
    - _description
```
java.lang.String _description
```
    - _model_category
```
java.lang.String _model_category
```
    - _scoring_time
```
long _scoring_time
```
    - _custom_metric_name
```
java.lang.String _custom_metric_name
```
    - _custom_metric_value
```
double _custom_metric_value
```
    - _r2
```
double _r2
```
    - _mae
```
double _mae
```
    - _MSE
```
double _MSE
```
    - _RMSE
```
double _RMSE
```
    - _nobs
```
long _nobs
```
- Class hex.genmodel.attributes.metrics.MojoModelMetricsAnomaly extends MojoModelMetrics implements Serializable
  - Serialized Fields
    - _mean_score
```
double _mean_score
```
    - _mean_normalized_score
```
double _mean_normalized_score
```
- Class hex.genmodel.attributes.metrics.MojoModelMetricsBinomial extends MojoModelMetricsSupervised implements Serializable
  - Serialized Fields
    - _auc
```
double _auc
```
    - _pr_auc
```
double _pr_auc
```
    - _gini
```
double _gini
```
    - _mean_per_class_error
```
double _mean_per_class_error
```
    - _logloss
```
double _logloss
```
    - _gains_lift_table
```
Table _gains_lift_table
```
    - _thresholds_and_metric_scores
```
Table _thresholds_and_metric_scores
```
    - _max_criteria_and_metric_scores
```
Table _max_criteria_and_metric_scores
```
    - _confusion_matrix
```
Table _confusion_matrix
```
- Class hex.genmodel.attributes.metrics.MojoModelMetricsBinomialGLM extends MojoModelMetricsBinomial implements Serializable
  - Serialized Fields
    - _nullDegreesOfFreedom
```
long _nullDegreesOfFreedom
```
    - _residualDegreesOfFreedom
```
long _residualDegreesOfFreedom
```
    - _resDev
```
double _resDev
```
    - _nullDev
```
double _nullDev
```
    - _AIC
```
double _AIC
```
    - _loglikelihood
```
double _loglikelihood
```
- Class hex.genmodel.attributes.metrics.MojoModelMetricsBinomialUplift extends MojoModelMetricsSupervised implements Serializable
  - Serialized Fields
    - _auuc
```
double _auuc
```
    - _normalized_auuc
```
double _normalized_auuc
```
    - _qini
```
double _qini
```
    - _ate
```
double _ate
```
    - _att
```
double _att
```
    - _atc
```
double _atc
```
    - _thresholds_and_metric_scores
```
Table _thresholds_and_metric_scores
```
    - _auuc_table
```
Table _auuc_table
```
    - _aecu_table
```
Table _aecu_table
```
- Class hex.genmodel.attributes.metrics.MojoModelMetricsMultinomial extends MojoModelMetricsSupervised implements Serializable
  - Serialized Fields
    - _confusion_matrix
```
Table _confusion_matrix
```
    - _hit_ratios
```
Table _hit_ratios
```
    - _multinomial_auc
```
Table _multinomial_auc
```
    - _multinomial_aucpr
```
Table _multinomial_aucpr
```
    - _logloss
```
double _logloss
```
    - _mean_per_class_error
```
double _mean_per_class_error
```
- Class hex.genmodel.attributes.metrics.MojoModelMetricsMultinomialGLM extends MojoModelMetricsMultinomial implements Serializable
  - Serialized Fields
    - _nullDegreesOfFreedom
```
long _nullDegreesOfFreedom
```
    - _residualDegreesOfFreedom
```
long _residualDegreesOfFreedom
```
    - _resDev
```
double _resDev
```
    - _nullDev
```
double _nullDev
```
    - _AIC
```
double _AIC
```
    - _loglikelihood
```
double _loglikelihood
```
- Class hex.genmodel.attributes.metrics.MojoModelMetricsOrdinal extends MojoModelMetricsSupervised implements Serializable
  - Serialized Fields
    - _hit_ratios
```
float[] _hit_ratios
```
    - _cm
```
Table _cm
```
    - _hit_ratio_table
```
Table _hit_ratio_table
```
    - _logloss
```
double _logloss
```
    - _mean_per_class_error
```
double _mean_per_class_error
```
    - _domain
```
java.lang.String[] _domain
```
    - _sigma
```
double _sigma
```
- Class hex.genmodel.attributes.metrics.MojoModelMetricsOrdinalGLM extends MojoModelMetricsOrdinal implements Serializable
  - Serialized Fields
    - _nullDegreesOfFreedom
```
long _nullDegreesOfFreedom
```
    - _residualDegreesOfFreedom
```
long _residualDegreesOfFreedom
```
    - _resDev
```
double _resDev
```
    - _nullDev
```
double _nullDev
```
    - _AIC
```
double _AIC
```
    - _loglikelihood
```
double _loglikelihood
```
- Class hex.genmodel.attributes.metrics.MojoModelMetricsRegression extends MojoModelMetricsSupervised implements Serializable
  - Serialized Fields
    - _mean_residual_deviance
```
double _mean_residual_deviance
```
    - _root_mean_squared_log_error
```
double _root_mean_squared_log_error
```
- Class hex.genmodel.attributes.metrics.MojoModelMetricsRegressionCoxPH extends MojoModelMetricsRegression implements Serializable
  - Serialized Fields
    - _concordance
```
double _concordance
```
    - _concordant
```
long _concordant
```
    - _discordant
```
long _discordant
```
    - _tied_y
```
long _tied_y
```
- Class hex.genmodel.attributes.metrics.MojoModelMetricsRegressionGLM extends MojoModelMetricsRegression implements Serializable
  - Serialized Fields
    - _nullDegreesOfFreedom
```
long _nullDegreesOfFreedom
```
    - _residualDegreesOfFreedom
```
long _residualDegreesOfFreedom
```
    - _resDev
```
double _resDev
```
    - _nullDev
```
double _nullDev
```
    - _AIC
```
double _AIC
```
    - _loglikelihood
```
double _loglikelihood
```
- Class hex.genmodel.attributes.metrics.MojoModelMetricsSupervised extends MojoModelMetrics implements Serializable
  - Serialized Fields
    - _sigma
```
double _sigma
```
Package hex.genmodel.attributes.parameters
- Class hex.genmodel.attributes.parameters.ColumnSpecifier extends java.lang.Object implements Serializable
  - Serialized Fields
    - columnName
```
java.lang.String columnName
```
    - is_member_of_frames
```
java.lang.String[] is_member_of_frames
```
- Class hex.genmodel.attributes.parameters.FeatureContribution extends java.lang.Object implements Serializable
  - Serialized Fields
    - columnName
```
java.lang.String columnName
```
    - shapleyContribution
```
double shapleyContribution
```
- Class hex.genmodel.attributes.parameters.KeyValue extends java.lang.Object implements Serializable
  - Serialized Fields
    - key
```
java.lang.String key
```
    - value
```
double value
```
- Class hex.genmodel.attributes.parameters.ModelParameter extends java.lang.Object implements Serializable
  - Serialized Fields
    - name
```
java.lang.String name
```
    - label
```
java.lang.String label
```
    - help
```
java.lang.String help
```
    - required
```
boolean required
```
    - type
```
java.lang.String type
```
    - default_value
```
java.lang.Object default_value
```
    - actual_value
```
java.lang.Object actual_value
```
    - input_value
```
java.lang.Object input_value
```
    - level
```
java.lang.String level
```
    - values
```
java.lang.String[] values
```
    - is_member_of_frames
```
java.lang.String[] is_member_of_frames
```
    - is_mutually_exclusive_with
```
java.lang.String[] is_mutually_exclusive_with
```
    - gridable
```
boolean gridable
```
- Class hex.genmodel.attributes.parameters.ParameterKey extends java.lang.Object implements Serializable
  - Serialized Fields
    - name
```
java.lang.String name
```
    - type
```
ParameterKey.Type type
```
    - URL
```
java.lang.String URL
```
- Class hex.genmodel.attributes.parameters.StringPair extends java.lang.Object implements Serializable
  - Serialized Fields
    - _a
```
java.lang.String _a
```
    - _b
```
java.lang.String _b
```

Package hex.genmodel.descriptor

Class hex.genmodel.descriptor.ModelDescriptorBuilder.MojoModelDescriptor extends java.lang.Object implements Serializable

Serialized Fields

_h2oVersion
```
java.lang.String _h2oVersion
```
_category
```
ModelCategory _category
```
_uuid
```
java.lang.String _uuid
```
_supervised
```
boolean _supervised
```
_nfeatures
```
int _nfeatures
```
_nclasses
```
int _nclasses
```
_balanceClasses
```
boolean _balanceClasses
```
_defaultThreshold
```
double _defaultThreshold
```
_priorClassDistrib
```
double[] _priorClassDistrib
```
_modelClassDistrib
```
double[] _modelClassDistrib
```
_offsetColumn
```
java.lang.String _offsetColumn
```
_foldColumn
```
java.lang.String _foldColumn
```
_weightsColumn
```
java.lang.String _weightsColumn
```
_treatmentColumn
```
java.lang.String _treatmentColumn
```
_domains
```
java.lang.String[][] _domains
```
_origDomains
```
java.lang.String[][] _origDomains
```
_names
```
java.lang.String[] _names
```
_origNames
```
java.lang.String[] _origNames
```
_algoName
```
java.lang.String _algoName
```
_fullAlgoName
```
java.lang.String _fullAlgoName
```

Class hex.genmodel.descriptor.ModelDescriptorBuilder.PojoModelDescriptor extends java.lang.Object implements Serializable
- Serialized Fields
  - _category
```
ModelCategory _category
```
  - _supervised
```
boolean _supervised
```
  - _nfeatures
```
int _nfeatures
```
  - _nclasses
```
int _nclasses
```
  - _offsetColumn
```
java.lang.String _offsetColumn
```
  - _domains
```
java.lang.String[][] _domains
```
  - _origDomains
```
java.lang.String[][] _origDomains
```
  - _names
```
java.lang.String[] _names
```
  - _origNames
```
java.lang.String[] _origNames
```

Package hex.genmodel.easy
- Class hex.genmodel.easy.BinaryEncoder extends java.lang.Object implements Serializable
  - Serialized Fields
    - columnName
```
java.lang.String columnName
```
    - targetIndex
```
int targetIndex
```
    - domainMap
```
java.util.Map<K,V> domainMap
```
    - binaryCategorySizes
```
int binaryCategorySizes
```
- Class hex.genmodel.easy.EasyPredictModelWrapper extends java.lang.Object implements Serializable
  - Serialized Fields
    - m
```
GenModel m
```
    - rowDataConverter
```
RowToRawDataConverter rowDataConverter
```
    - useExtendedOutput
```
boolean useExtendedOutput
```
    - enableLeafAssignment
```
boolean enableLeafAssignment
```
    - enableGLRMReconstruct
```
boolean enableGLRMReconstruct
```
    - enableStagedProbabilities
```
boolean enableStagedProbabilities
```
    - enableContributions
```
boolean enableContributions
```
    - glrmIterNumber
```
int glrmIterNumber
```
    - predictContributions
```
PredictContributions predictContributions
```
- Class hex.genmodel.easy.EasyPredictModelWrapper.ErrorConsumer extends java.lang.Object implements Serializable
- Class hex.genmodel.easy.EigenEncoder extends java.lang.Object implements Serializable
  - Serialized Fields
    - columnName
```
java.lang.String columnName
```
    - targetIndex
```
int targetIndex
```
    - domainMap
```
java.util.Map<K,V> domainMap
```
    - projectionEigenVec
```
double[] projectionEigenVec
```
- Class hex.genmodel.easy.EnumEncoder extends java.lang.Object implements Serializable
  - Serialized Fields
    - columnName
```
java.lang.String columnName
```
    - targetIndex
```
int targetIndex
```
    - domainMap
```
java.util.Map<K,V> domainMap
```
- Class hex.genmodel.easy.EnumLimitedEncoder extends java.lang.Object implements Serializable
  - Serialized Fields
    - columnName
```
java.lang.String columnName
```
    - targetIndex
```
int targetIndex
```
    - domainMap
```
java.util.Map<K,V> domainMap
```
- Class hex.genmodel.easy.LabelEncoder extends java.lang.Object implements Serializable
  - Serialized Fields
    - targetIndex
```
int targetIndex
```
    - domainMap
```
java.util.Map<K,V> domainMap
```
- Class hex.genmodel.easy.OneHotEncoder extends java.lang.Object implements Serializable
  - Serialized Fields
    - columnName
```
java.lang.String columnName
```
    - targetIndex
```
int targetIndex
```
    - domainMap
```
java.util.Map<K,V> domainMap
```
- Class hex.genmodel.easy.RowData extends java.util.HashMap<java.lang.String,java.lang.Object> implements Serializable
- Class hex.genmodel.easy.RowToRawDataConverter extends java.lang.Object implements Serializable
  - Serialized Fields
    - _modelColumnNameToIndexMap
```
java.util.Map<K,V> _modelColumnNameToIndexMap
```
    - _domainMap
```
java.util.Map<K,V> _domainMap
```
    - _errorConsumer
```
EasyPredictModelWrapper.ErrorConsumer _errorConsumer
```
    - _convertUnknownCategoricalLevelsToNa
```
boolean _convertUnknownCategoricalLevelsToNa
```
    - _convertInvalidNumbersToNa
```
boolean _convertInvalidNumbersToNa
```
Package hex.genmodel.easy.error
- Class hex.genmodel.easy.error.CountingErrorConsumer extends EasyPredictModelWrapper.ErrorConsumer implements Serializable
  - Serialized Fields
    - dataTransformationErrorsCountPerColumn
```
java.util.Map<K,V> dataTransformationErrorsCountPerColumn
```
    - unknownCategoricalsPerColumn
```
java.util.Map<K,V> unknownCategoricalsPerColumn
```
    - unseenCategoricalsCollector
```
java.util.Map<K,V> unseenCategoricalsCollector
```
    - collectUnseenCategoricals
```
boolean collectUnseenCategoricals
```
- Class hex.genmodel.easy.error.VoidErrorConsumer extends EasyPredictModelWrapper.ErrorConsumer implements Serializable
Package hex.genmodel.easy.exception
- Class hex.genmodel.easy.exception.PredictException extends java.lang.Exception implements Serializable
- Class hex.genmodel.easy.exception.PredictNumberFormatException extends PredictException implements Serializable
- Class hex.genmodel.easy.exception.PredictUnknownCategoricalLevelException extends PredictException implements Serializable
  - Serialized Fields
    - columnName
```
java.lang.String columnName
```
    - unknownLevel
```
java.lang.String unknownLevel
```
- Class hex.genmodel.easy.exception.PredictUnknownTypeException extends PredictException implements Serializable
- Class hex.genmodel.easy.exception.PredictWrongModelCategoryException extends PredictException implements Serializable
Package hex.genmodel.easy.prediction
- Class hex.genmodel.easy.prediction.AbstractPrediction extends java.lang.Object implements Serializable
- Class hex.genmodel.easy.prediction.AnomalyDetectionPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - isAnomaly
```
java.lang.Boolean isAnomaly
```
      Only available when MojoModel has contamination parameter defined otherwise is null.
    - score
```
double score
```
      The raw number that an algorithm is using to count final anomaly score. E.g. for Isolation Forest this number is mean path length of data in the trees. Smaller number means more anomalous point, higher number means more normal point.
    - normalizedScore
```
double normalizedScore
```
      Higher number means more anomalous point, smaller number means more normal point.
    - leafNodeAssignments
```
java.lang.String[] leafNodeAssignments
```
      Only valid for tree-based models, null for all other mojo models.
    - leafNodeAssignmentIds
```
int[] leafNodeAssignmentIds
```
      Ditto, available in MOJO 1.3 and newer
    - stageProbabilities
```
double[] stageProbabilities
```
      Staged predictions of tree algorithms (prediction probabilities of trees per iteration). The output structure is for tree Tt and class Cc: Binomial models: [probability T1.C1, probability T2.C1, ..., Tt.C1] where Tt.C1 correspond to the the probability p0 Multinomial models: [probability T1.C1, probability T1.C2, ..., Tt.Cc]
- Class hex.genmodel.easy.prediction.AutoEncoderModelPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - original
```
double[] original
```
      Representation of the original input the way AutoEncoder model sees it (1-hot encoded categorical values)
    - reconstructed
```
double[] reconstructed
```
      Reconstructed data, the array has same length as the original input. The user can use the original input and reconstructed output to easily calculate eg. the reconstruction error.
    - reconstructedRowData
```
RowData reconstructedRowData
```
      Reconstructed data represented in RowData structure. The structure will copy the structure of the RowData input with the exception of categorical values. Categorical fields will be represented as a map of the domain values to the reconstructed values. Example: input RowData([sex: "Male", ..]) will produce output RowData([sex: [Male: 0.9, Female: 0.1], ..]
    - mse
```
double mse
```
      Reconstruction mean squared error calculated from original and reconstructed data. Uses a normalization defined for the numerical features of the trained model. average reconstruction error = ||original - reconstructed||^2 / length(original)
- Class hex.genmodel.easy.prediction.BinomialModelPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - labelIndex
```
int labelIndex
```
      0 or 1.
    - label
```
java.lang.String label
```
      Label of the predicted level.
    - classProbabilities
```
double[] classProbabilities
```
      This array of length two has the class probability for each class (aka categorical or factor level) in the response column. The array corresponds to the level names returned by:
      model.getDomainValues(model.getResponseIdx())
      "Domain" is the internal H2O term for level names. The values in this array may be Double.NaN, which means NA (this will happen with GLM, for example, if one of the input values for a new data point is NA). If they are valid numeric values, then they will sum up to 1.0.
    - calibratedClassProbabilities
```
double[] calibratedClassProbabilities
```
      Class probabilities calibrated by Platt Scaling or Isotonic Regression. Optional, only calculated if the model supports it.
    - leafNodeAssignments
```
java.lang.String[] leafNodeAssignments
```
    - leafNodeAssignmentIds
```
int[] leafNodeAssignmentIds
```
    - stageProbabilities
```
double[] stageProbabilities
```
      Staged predictions of tree algorithms (prediction probabilities of trees per iteration). The output structure is for tree Tt and class Cc: Binomial models: [probability T1.C1, probability T2.C1, ..., Tt.C1] where Tt.C1 correspond to the the probability p0 Multinomial models: [probability T1.C1, probability T1.C2, ..., Tt.Cc]
    - contributions
```
float[] contributions
```
      Per-feature prediction contributions (SHAP values). Size of the returned array is #features + 1 - there is a feature contribution column for each input feature, the last item is the model bias. The sum of the feature contributions and the bias term is equal to the raw prediction of the model. Raw prediction of tree-based model is the sum of the predictions of the individual trees before the inverse link function is applied to get the actual prediction. This means the sum is not equal to the probabilities returned in classProbabilities. (Optional) Available only for supported models (GBM, XGBoost).
- Class hex.genmodel.easy.prediction.ClusteringModelPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - cluster
```
int cluster
```
      Chosen cluster for this data point.
    - distances
```
double[] distances
```
      (Optional) Vector of squared distances to all cluster centers. This field will only be included in the output if "useExtendedOutput" flag was enabled in EasyPredictModelWrapper.
- Class hex.genmodel.easy.prediction.CoxPHModelPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - value
```
double value
```
      This value may be Double.NaN, which means NA (this will happen with CoxPH, for example, if one of the input values for a new data point is NA).
- Class hex.genmodel.easy.prediction.DimReductionModelPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - dimensions
```
double[] dimensions
```
    - reconstructed
```
double[] reconstructed
```
      This field is only used for GLRM and not for PCA. Reconstructed data, the array has same length as the original input. The user can use the original input and reconstructed output to easily calculate eg. the reconstruction error. Note that all values are either doubles or integers. Users need to convert the enum columns from the integer columns if necessary.
- Class hex.genmodel.easy.prediction.KLimeModelPrediction extends RegressionModelPrediction implements Serializable
  - Serialized Fields
    - cluster
```
int cluster
```
      Chosen cluster for this data point.
    - reasonCodes
```
double[] reasonCodes
```
      Array of reason codes. Each element of the array corresponds to a feature used in model training. Order of the codes is given by the order of columns in the model.
- Class hex.genmodel.easy.prediction.MultinomialModelPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - labelIndex
```
int labelIndex
```
      Index number of the predicted class (aka categorical or factor level) in the response column.
    - label
```
java.lang.String label
```
      Label of the predicted level.
    - classProbabilities
```
double[] classProbabilities
```
      This array has an element for each class (aka categorical or factor level) in the response column. The array corresponds to the level names returned by:
      model.getDomainValues(model.getResponseIdx())
      "Domain" is the internal H2O term for level names. The values in this array may be Double.NaN, which means NA. If they are valid numeric values, then they will sum up to 1.0.
    - leafNodeAssignments
```
java.lang.String[] leafNodeAssignments
```
    - leafNodeAssignmentIds
```
int[] leafNodeAssignmentIds
```
    - stageProbabilities
```
double[] stageProbabilities
```
      Staged predictions of tree algorithms (prediction probabilities of trees per iteration). The output structure is for tree Tt and class Cc: Binomial models: [probability T1.C1, probability T2.C1, ..., Tt.C1] where Tt.C1 correspond to the the probability p0 Multinomial models: [probability T1.C1, probability T1.C2, ..., Tt.Cc]
- Class hex.genmodel.easy.prediction.OrdinalModelPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - labelIndex
```
int labelIndex
```
      Index number of the predicted class (aka categorical or factor level) in the response column.
    - label
```
java.lang.String label
```
      Label of the predicted level.
    - classProbabilities
```
double[] classProbabilities
```
      This array has an element for each class (aka categorical or factor level) in the response column. The array corresponds to the level names returned by:
      model.getDomainValues(model.getResponseIdx())
      "Domain" is the internal H2O term for level names. The values in this array may be Double.NaN, which means NA. If they are valid numeric values, then they will sum up to 1.0.
- Class hex.genmodel.easy.prediction.RegressionModelPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - value
```
double value
```
      This value may be Double.NaN, which means NA (this will happen with GLM, for example, if one of the input values for a new data point is NA).
    - leafNodeAssignments
```
java.lang.String[] leafNodeAssignments
```
    - leafNodeAssignmentIds
```
int[] leafNodeAssignmentIds
```
    - stageProbabilities
```
double[] stageProbabilities
```
      Staged predictions of tree algorithms (prediction probabilities of trees per iteration). The output structure is for tree Tt and class Cc: Binomial models: [probability T1.C1, probability T2.C1, ..., Tt.C1] where Tt.C1 correspond to the the probability p0 Multinomial models: [probability T1.C1, probability T1.C2, ..., Tt.Cc]
    - contributions
```
float[] contributions
```
      Per-feature prediction contributions (SHAP values). Size of the returned array is #features + 1 - there is a feature contribution column for each input feature, the last item is the model bias. The sum of the feature contributions and the bias term is equal to the raw prediction of the model. Raw prediction of tree-based model is the sum of the predictions of the individual trees before the inverse link function is applied to get the actual prediction. For Gaussian distribution the sum of the contributions is equal to the model prediction. (Optional) Available only for supported models (GBM, XGBoost).
- Class hex.genmodel.easy.prediction.TargetEncoderPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - transformations
```
double[] transformations
```
- Class hex.genmodel.easy.prediction.UpliftBinomialModelPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - predictions
```
double[] predictions
```
- Class hex.genmodel.easy.prediction.Word2VecPrediction extends AbstractPrediction implements Serializable
  - Serialized Fields
    - wordEmbeddings
```
java.util.HashMap<K,V> wordEmbeddings
```

Serialized Form

Package hex.genmodel

Class hex.genmodel.GenModel extends java.lang.Object implements Serializable

Serialized Fields

_names

_domains

_responseColumn

_offsetColumn

_foldColumn

_treatmentColumn

Class hex.genmodel.GenMunger extends java.lang.Object implements Serializable

Serialized Fields

_steps

Class hex.genmodel.GenMunger.Step extends java.lang.Object implements Serializable

Serialized Fields

_names

_types

_outNames

_params

Class hex.genmodel.MojoModel extends GenModel implements Serializable

Serialized Fields

_algoName

_h2oVersion

_category

_uuid

_supervised

_nfeatures

_nclasses

_balanceClasses

_defaultThreshold

_priorClassDistrib

_modelClassDistrib

_mojo_version

_modelDescriptor

_modelAttributes

_reproducibilityInformation

Package hex.genmodel.algos.coxph

Class hex.genmodel.algos.coxph.CoxPHMojoModel extends MojoModel implements Serializable

Serialized Fields

_coef

_strata

_strata_len

_x_mean_cat

_x_mean_num

_cat_offsets

_cats

_lpBase

_useAllFactorLevels

_nums

_interactions_1

_interactions_2

_interaction_targets

_is_enum_1

_interaction_column_index

_interaction_column_domains

_interaction_types

_num_offsets

Package hex.genmodel.algos.deeplearning

Class hex.genmodel.algos.deeplearning.ActivationUtils.ExpRectifierDropoutOut extends ActivationUtils.ExpRectifierOut implements Serializable

Class hex.genmodel.algos.deeplearning.ActivationUtils.ExpRectifierOut extends java.lang.Object implements Serializable

Class hex.genmodel.algos.deeplearning.ActivationUtils.LinearOut extends java.lang.Object implements Serializable

Class hex.genmodel.algos.deeplearning.ActivationUtils.MaxoutDropoutOut extends ActivationUtils.MaxoutOut implements Serializable

Class hex.genmodel.algos.deeplearning.ActivationUtils.MaxoutOut extends java.lang.Object implements Serializable

Class hex.genmodel.algos.deeplearning.ActivationUtils.RectifierDropoutOut extends ActivationUtils.RectifierOut implements Serializable

Class hex.genmodel.algos.deeplearning.ActivationUtils.RectifierOut extends java.lang.Object implements Serializable

Class hex.genmodel.algos.deeplearning.ActivationUtils.SoftmaxOut extends java.lang.Object implements Serializable

Class hex.genmodel.algos.deeplearning.ActivationUtils.TanhDropoutOut extends ActivationUtils.TanhOut implements Serializable

Class hex.genmodel.algos.deeplearning.ActivationUtils.TanhOut extends java.lang.Object implements Serializable

Class hex.genmodel.algos.deeplearning.DeeplearningMojoModel extends MojoModel implements Serializable

Serialized Fields

_mini_batch_size

_nums

_cats

_catoffsets

_normmul

_normsub

_normrespmul

_normrespsub

_use_all_factor_levels

_activation