Parameters of H2OKMeans

Affected Class

• ai.h2o.sparkling.ml.algos.H2OKMeans

Parameters

• Each parameter has also a corresponding getter and setter method. (E.g.: label -> getLabel() , setLabel(...) )

ignoredCols

Names of columns to ignore for training.

Scala default value: null ; Python default value: None

Also available on the trained model.

userPoints

This option allows you to specify array of points, where each point represents coordinates of an initial cluster center. The user-specified points must have the same number of columns as the training observations. The number of rows must equal the number of clusters.

Scala default value: null ; Python default value: None

categoricalEncoding

Encoding scheme for categorical features. Possible values are "AUTO", "OneHotInternal", "OneHotExplicit", "Enum", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited".

Default value: "AUTO"

Also available on the trained model.

clusterSizeConstraints

An array specifying the minimum number of points that should be in each cluster. The length of the constraints array has to be the same as the number of clusters.

Scala default value: null ; Python default value: None

Also available on the trained model.

columnsToCategorical

List of columns to convert to categorical before modelling

Scala default value: Array() ; Python default value: []

convertInvalidNumbersToNa

If set to ‘true’, the model converts invalid numbers to NA during making predictions.

Scala default value: false ; Python default value: False

Also available on the trained model.

convertUnknownCategoricalLevelsToNa

If set to ‘true’, the model converts unknown categorical levels to NA during making predictions.

Scala default value: false ; Python default value: False

Also available on the trained model.

detailedPredictionCol

Column containing additional prediction details, its content depends on the model type.

Default value: "detailed_prediction"

Also available on the trained model.

estimateK

Whether to estimate the number of clusters (<=k) iteratively and deterministically.

Scala default value: false ; Python default value: False

Also available on the trained model.

exportCheckpointsDir

Automatically export generated models to this directory.

Scala default value: null ; Python default value: None

Also available on the trained model.

featuresCols

Name of feature columns

Scala default value: Array() ; Python default value: []

Also available on the trained model.

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. Possible values are "AUTO", "Random", "Modulo", "Stratified".

Default value: "AUTO"

Also available on the trained model.

foldCol

Column with cross-validation fold index assignment per observation.

Scala default value: null ; Python default value: None

Also available on the trained model.

ignoreConstCols

Ignore constant columns.

Scala default value: true ; Python default value: True

Also available on the trained model.

init

Initialization mode. Possible values are "Random", "PlusPlus", "Furthest", "User".

Default value: "Furthest"

Also available on the trained model.

k

The max. number of clusters. If estimate_k is disabled, the model will find k centroids, otherwise it will find up to k centroids.

Default value: 1

Also available on the trained model.

keepBinaryModels

If set to true, all binary models created during execution of the fit method will be kept in DKV of H2O-3 cluster.

Scala default value: false ; Python default value: False

keepCrossValidationFoldAssignment

Whether to keep the cross-validation fold assignment.

Scala default value: false ; Python default value: False

Also available on the trained model.

keepCrossValidationModels

Whether to keep the cross-validation models.

Scala default value: true ; Python default value: True

Also available on the trained model.

keepCrossValidationPredictions

Whether to keep the predictions of the cross-validation models.

Scala default value: false ; Python default value: False

Also available on the trained model.

maxIterations

Maximum training iterations (if estimate_k is enabled, then this is for each inner Lloyds iteration).

Default value: 10

Also available on the trained model.

maxRuntimeSecs

Maximum allowed runtime in seconds for model training. Use 0 to disable.

Default value: 0.0

Also available on the trained model.

modelId

Destination id for this model; auto-generated if not specified.

Scala default value: null ; Python default value: None

namedMojoOutputColumns

Mojo Output is not stored in the array but in the properly named columns

Scala default value: true ; Python default value: True

Also available on the trained model.

nfolds

Number of folds for K-fold cross-validation (0 to disable or >= 2).

Default value: 0

Also available on the trained model.

predictionCol

Prediction column name

Default value: "prediction"

Also available on the trained model.

scoreEachIteration

Whether to score during each iteration of model training.

Scala default value: false ; Python default value: False

Also available on the trained model.

seed

RNG Seed.

Scala default value: -1L ; Python default value: -1

Also available on the trained model.

splitRatio

Accepts values in range [0, 1.0] which determine how large part of dataset is used for training and for validation. For example, 0.8 -> 80% training 20% validation. This parameter is ignored when validationDataFrame is set.

Default value: 1.0

standardize

Standardize columns before computing distances.

Scala default value: true ; Python default value: True

Also available on the trained model.

validationDataFrame

A data frame dedicated for a validation of the trained model. If the parameters is not set,a validation frame created via the ‘splitRatio’ parameter.

Scala default value: null ; Python default value: None

withContributions

Enables or disables generating a sub-column of detailedPredictionCol containing Shapley values.

Scala default value: false ; Python default value: False

Also available on the trained model.

withLeafNodeAssignments

Enables or disables computation of leaf node assignments.

Scala default value: false ; Python default value: False

Also available on the trained model.

withStageResults

Enables or disables computation of stage results.

Scala default value: false ; Python default value: False

Also available on the trained model.