ModelParametersSchemaV3 (h2o-bindings version 3.24.0.3 API)

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

Direct Known Subclasses:

AggregatorParametersV99, ClusteringModelParametersSchemaV3, CoxPHParametersV3, DeepLearningParametersV3, DeepWaterParametersV3, ExampleParametersV3, GenericParametersV3, GLMParametersV3, GLRMParametersV3, GrepParametersV3, NaiveBayesParametersV3, PCAParametersV3, QuantileParametersV3, SharedTreeParametersV3, StackedEnsembleParametersV99, SVDParametersV99, Word2VecParametersV3, XGBoostParametersV3
```
public class ModelParametersSchemaV3
extends SchemaV3
```

Field Summary

Fields
Modifier and Type	Field and Description
`ModelParametersCategoricalEncodingScheme`	`categoricalEncoding` Encoding scheme for categorical features
`ModelKeyV3`	`checkpoint` Model checkpoint to resume training with.
`java.lang.String`	`customMetricFunc` Reference to custom evaluation function, format: `language:keyName=funcName`
`GenmodelutilsDistributionFamily`	`distribution` Distribution function
`java.lang.String`	`exportCheckpointsDir` Automatically export generated models to this directory.
`ModelParametersFoldAssignmentScheme`	`foldAssignment` Cross-validation fold assignment scheme, if fold_column is not specified.
`ColSpecifierV3`	`foldColumn` Column with cross-validation fold index assignment per observation.
`double`	`huberAlpha` Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
`boolean`	`ignoreConstCols` Ignore constant columns.
`java.lang.String[]`	`ignoredColumns` Names of columns to ignore for training.
`boolean`	`keepCrossValidationFoldAssignment` Whether to keep the cross-validation fold assignment.
`boolean`	`keepCrossValidationModels` Whether to keep the cross-validation models.
`boolean`	`keepCrossValidationPredictions` Whether to keep the predictions of the cross-validation models.
`int`	`maxCategoricalLevels` For every categorical feature, only use this many most frequent categorical levels for model training.
`double`	`maxRuntimeSecs` Maximum allowed runtime in seconds for model training.
`ModelKeyV3`	`modelId` Destination id for this model; auto-generated if not specified.
`int`	`nfolds` Number of folds for K-fold cross-validation (0 to disable or >= 2).
`ColSpecifierV3`	`offsetColumn` Offset column.
`boolean`	`parallelizeCrossValidation` Allow parallel training of cross-validation models
`double`	`quantileAlpha` Desired quantile for Quantile regression, must be between 0 and 1.
`ColSpecifierV3`	`responseColumn` Response variable column.
`boolean`	`scoreEachIteration` Whether to score during each iteration of model training.
`ScoreKeeperStoppingMetric`	`stoppingMetric` Metric to use for early stopping (AUTO: logloss for classification, deviance for regression).
`int`	`stoppingRounds` Early stopping based on convergence of stopping_metric.
`double`	`stoppingTolerance` Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
`FrameKeyV3`	`trainingFrame` Id of the training data frame.
`double`	`tweediePower` Tweedie power for Tweedie regression, must be between 1 and 2.
`FrameKeyV3`	`validationFrame` Id of the validation data frame.
`ColSpecifierV3`	`weightsColumn` Column with observation weights.

Constructor Summary

Constructors
Constructor and Description

ModelParametersSchemaV3()
Public constructor

Constructors
Constructor and Description
`ModelParametersSchemaV3()` 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
  - modelId
```
@SerializedName(value="model_id")
public ModelKeyV3 modelId
```
    Destination id for this model; auto-generated if not specified.
  - trainingFrame
```
@SerializedName(value="training_frame")
public FrameKeyV3 trainingFrame
```
    Id of the training data frame.
  - validationFrame
```
@SerializedName(value="validation_frame")
public FrameKeyV3 validationFrame
```
    Id of the validation data frame.
  - nfolds
```
public int nfolds
```
    Number of folds for K-fold cross-validation (0 to disable or >= 2).
  - keepCrossValidationModels
```
@SerializedName(value="keep_cross_validation_models")
public boolean keepCrossValidationModels
```
    Whether to keep the cross-validation models.
  - keepCrossValidationPredictions
```
@SerializedName(value="keep_cross_validation_predictions")
public boolean keepCrossValidationPredictions
```
    Whether to keep the predictions of the cross-validation models.
  - keepCrossValidationFoldAssignment
```
@SerializedName(value="keep_cross_validation_fold_assignment")
public boolean keepCrossValidationFoldAssignment
```
    Whether to keep the cross-validation fold assignment.
  - parallelizeCrossValidation
```
@SerializedName(value="parallelize_cross_validation")
public boolean parallelizeCrossValidation
```
    Allow parallel training of cross-validation models
  - distribution
```
public GenmodelutilsDistributionFamily distribution
```
    Distribution function
  - tweediePower
```
@SerializedName(value="tweedie_power")
public double tweediePower
```
    Tweedie power for Tweedie regression, must be between 1 and 2.
  - quantileAlpha
```
@SerializedName(value="quantile_alpha")
public double quantileAlpha
```
    Desired quantile for Quantile regression, must be between 0 and 1.
  - huberAlpha
```
@SerializedName(value="huber_alpha")
public double huberAlpha
```
    Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
  - responseColumn
```
@SerializedName(value="response_column")
public ColSpecifierV3 responseColumn
```
    Response variable column.
  - weightsColumn
```
@SerializedName(value="weights_column")
public ColSpecifierV3 weightsColumn
```
    Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor.
  - offsetColumn
```
@SerializedName(value="offset_column")
public ColSpecifierV3 offsetColumn
```
    Offset column. This will be added to the combination of columns before applying the link function.
  - foldColumn
```
@SerializedName(value="fold_column")
public ColSpecifierV3 foldColumn
```
    Column with cross-validation fold index assignment per observation.
  - foldAssignment
```
@SerializedName(value="fold_assignment")
public ModelParametersFoldAssignmentScheme foldAssignment
```
    Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems.
  - categoricalEncoding
```
@SerializedName(value="categorical_encoding")
public ModelParametersCategoricalEncodingScheme categoricalEncoding
```
    Encoding scheme for categorical features
  - maxCategoricalLevels
```
@SerializedName(value="max_categorical_levels")
public int maxCategoricalLevels
```
    For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited.
  - ignoredColumns
```
@SerializedName(value="ignored_columns")
public java.lang.String[] ignoredColumns
```
    Names of columns to ignore for training.
  - ignoreConstCols
```
@SerializedName(value="ignore_const_cols")
public boolean ignoreConstCols
```
    Ignore constant columns.
  - scoreEachIteration
```
@SerializedName(value="score_each_iteration")
public boolean scoreEachIteration
```
    Whether to score during each iteration of model training.
  - checkpoint
```
public ModelKeyV3 checkpoint
```
    Model checkpoint to resume training with.
  - stoppingRounds
```
@SerializedName(value="stopping_rounds")
public int stoppingRounds
```
    Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
  - maxRuntimeSecs
```
@SerializedName(value="max_runtime_secs")
public double maxRuntimeSecs
```
    Maximum allowed runtime in seconds for model training. Use 0 to disable.
  - stoppingMetric
```
@SerializedName(value="stopping_metric")
public ScoreKeeperStoppingMetric stoppingMetric
```
    Metric to use for early stopping (AUTO: logloss for classification, deviance for regression). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
  - stoppingTolerance
```
@SerializedName(value="stopping_tolerance")
public double stoppingTolerance
```
    Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
  - customMetricFunc
```
@SerializedName(value="custom_metric_func")
public java.lang.String customMetricFunc
```
    Reference to custom evaluation function, format: `language:keyName=funcName`
  - exportCheckpointsDir
```
@SerializedName(value="export_checkpoints_dir")
public java.lang.String exportCheckpointsDir
```
    Automatically export generated models to this directory.
- Constructor Detail
  - ModelParametersSchemaV3
```
public ModelParametersSchemaV3()
```
    Public constructor
- Method Detail
  - toString
```
public java.lang.String toString()
```
    Return the contents of this object as a JSON String.
    
    Overrides:
    
    toString in class SchemaV3

Class ModelParametersSchemaV3

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

modelId

trainingFrame

validationFrame

nfolds

keepCrossValidationModels

keepCrossValidationPredictions

keepCrossValidationFoldAssignment

parallelizeCrossValidation

distribution

tweediePower

quantileAlpha

huberAlpha

responseColumn

weightsColumn

offsetColumn

foldColumn

foldAssignment

categoricalEncoding

maxCategoricalLevels

ignoredColumns

ignoreConstCols

scoreEachIteration

checkpoint

stoppingRounds

maxRuntimeSecs

stoppingMetric

stoppingTolerance

customMetricFunc

exportCheckpointsDir

Constructor Detail

ModelParametersSchemaV3

Method Detail

toString