.. _parameters_H2OGLM:
Parameters of H2OGLM
--------------------
Affected Classes
################
- ``ai.h2o.sparkling.ml.algos.H2OGLM``
- ``ai.h2o.sparkling.ml.algos.classification.H2OGLMClassifier``
- ``ai.h2o.sparkling.ml.algos.regression.H2OGLMRegressor``
Parameters
##########
- *Each parameter has also a corresponding getter and setter method.*
*(E.g.:* ``label`` *->* ``getLabel()`` *,* ``setLabel(...)`` *)*
HGLM
If set to true, will return HGLM model. Otherwise, normal GLM model will be returned.
*Scala default value:* ``false`` *; Python default value:* ``False``
*Also available on the trained model.*
betaConstraints
Data frame of beta constraints enabling to set special conditions over the model coefficients.
*Scala default value:* ``null`` *; Python default value:* ``None``
ignoredCols
Names of columns to ignore for training.
*Scala default value:* ``null`` *; Python default value:* ``None``
*Also available on the trained model.*
interactionPairs
A list of pairwise (first order) column interactions.
*Scala default value:* ``null`` *; Python default value:* ``None``
plugValues
A map containing values that will be used to impute missing values of the training/validation frame, use with conjunction missingValuesHandling = "PlugValues")
*Scala default value:* ``null`` *; Python default value:* ``None``
randomCols
Names of random columns for HGLM.
*Scala default value:* ``null`` *; Python default value:* ``None``
alphaValue
Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = 'L-BFGS'; 0.5 otherwise.
*Scala default value:* ``null`` *; Python default value:* ``None``
*Also available on the trained model.*
aucType
Set default multinomial AUC type. Possible values are ``"AUTO"``, ``"NONE"``, ``"MACRO_OVR"``, ``"WEIGHTED_OVR"``, ``"MACRO_OVO"``, ``"WEIGHTED_OVO"``.
*Default value:* ``"AUTO"``
*Also available on the trained model.*
balanceClasses
Balance training data class counts via over/under-sampling (for imbalanced data).
*Scala default value:* ``false`` *; Python default value:* ``False``
*Also available on the trained model.*
betaEpsilon
Converge if beta changes less (using L-infinity norm) than beta esilon, ONLY applies to IRLSM solver .
*Scala default value:* ``1.0e-4`` *; Python default value:* ``1.0E-4``
*Also available on the trained model.*
buildNullModel
If set, will build a model with only the intercept. Default to false.
*Scala default value:* ``false`` *; Python default value:* ``False``
*Also available on the trained model.*
calcLike
if true, will return likelihood function value.
*Scala default value:* ``false`` *; Python default value:* ``False``
*Also available on the trained model.*
checkpoint
Model checkpoint to resume training with.
*Scala default value:* ``null`` *; Python default value:* ``None``
*Also available on the trained model.*
classSamplingFactors
Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.
*Scala default value:* ``null`` *; Python default value:* ``None``
*Also available on the trained model.*
coldStart
Only applicable to multiple alpha/lambda values. If false, build the next model for next set of alpha/lambda values starting from the values provided by current model. If true will start GLM model from scratch.
*Scala default value:* ``false`` *; Python default value:* ``False``
*Also available on the trained model.*
columnsToCategorical
List of columns to convert to categorical before modelling
*Scala default value:* ``Array()`` *; Python default value:* ``[]``
computePValues
Request p-values computation, p-values work only with IRLSM solver and no regularization.
*Scala default value:* ``false`` *; Python default value:* ``False``
*Also available on the trained model.*
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.*
customMetricFunc
Reference to custom evaluation function, format: `language:keyName=funcName`.
*Scala default value:* ``null`` *; Python default value:* ``None``
*Also available on the trained model.*
dataFrameSerializer
A full name of a serializer used for serialization and deserialization of Spark DataFrames to a JSON value within NullableDataFrameParam.
*Default value:* ``"ai.h2o.sparkling.utils.JSONDataFrameSerializer"``
*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.*
dispersionEpsilon
If changes in dispersion parameter estimation or loglikelihood value is smaller than dispersion_epsilon, will break out of the dispersion parameter estimation loop using maximum likelihood.
*Scala default value:* ``1.0e-4`` *; Python default value:* ``1.0E-4``
*Also available on the trained model.*
dispersionLearningRate
Dispersion learning rate is only valid for tweedie family dispersion parameter estimation using ml. It must be > 0. This controls how much the dispersion parameter estimate is to be changed when the calculated loglikelihood actually decreases with the new dispersion. In this case, instead of setting new dispersion = dispersion + change, we set new dispersion = dispersion + dispersion_learning_rate * change. Defaults to 0.5.
*Default value:* ``0.5``
*Also available on the trained model.*
dispersionParameterMethod
Method used to estimate the dispersion parameter for Tweedie, Gamma and Negative Binomial only. Possible values are ``"pearson"``, ``"ml"``, ``"deviance"``.
*Default value:* ``"pearson"``
*Also available on the trained model.*
earlyStopping
Stop early when there is no more relative improvement on train or validation (if provided).
*Scala default value:* ``true`` *; Python default value:* ``True``
*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.*
family
Family. Use binomial for classification with logistic regression, others are for regression problems. Possible values are ``"AUTO"``, ``"gaussian"``, ``"binomial"``, ``"fractionalbinomial"``, ``"quasibinomial"``, ``"poisson"``, ``"gamma"``, ``"multinomial"``, ``"tweedie"``, ``"ordinal"``, ``"negativebinomial"``.
*Default value:* ``"AUTO"``
*Also available on the trained model.*
featuresCols
Name of feature columns
*Scala default value:* ``Array()`` *; Python default value:* ``[]``
*Also available on the trained model.*
fixDispersionParameter
Only used for Tweedie, Gamma and Negative Binomial GLM. If set, will use the dispsersion parameter in init_dispersion_parameter as the standard error and use it to calculate the p-values. Default to false.
*Scala default value:* ``false`` *; Python default value:* ``False``
*Also available on the trained model.*
fixTweedieVariancePower
If true, will fix tweedie variance power value to the value set in tweedie_variance_power.
*Scala default value:* ``true`` *; Python default value:* ``True``
*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.*
generateScoringHistory
If set to true, will generate scoring history for GLM. This may significantly slow down the algo.
*Scala default value:* ``false`` *; Python default value:* ``False``
*Also available on the trained model.*
generateVariableInflationFactors
if true, will generate variable inflation factors for numerical predictors. Default to false.
*Scala default value:* ``false`` *; Python default value:* ``False``
*Also available on the trained model.*
gradientEpsilon
Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver. Default (of -1.0) indicates: If lambda_search is set to False and lambda is equal to zero, the default value of gradient_epsilon is equal to .000001, otherwise the default value is .0001. If lambda_search is set to True, the conditional values above are 1E-8 and 1E-6 respectively.
*Default value:* ``-1.0``
*Also available on the trained model.*
ignoreConstCols
Ignore constant columns.
*Scala default value:* ``true`` *; Python default value:* ``True``
*Also available on the trained model.*
influence
If set to dfbetas will calculate the difference in beta when a datarow is included and excluded in the dataset. Possible values are ``"dfbetas"``.
*Scala default value:* ``null`` *; Python default value:* ``None``
*Also available on the trained model.*
initDispersionParameter
Only used for Tweedie, Gamma and Negative Binomial GLM. Store the initial value of dispersion parameter. If fix_dispersion_parameter is set, this value will be used in the calculation of p-values.Default to 1.0.
*Default value:* ``1.0``
*Also available on the trained model.*
interactions
A list of predictor column indices to interact. All pairwise combinations will be computed for the list.
*Scala default value:* ``null`` *; Python default value:* ``None``
*Also available on the trained model.*
intercept
Include constant term in the model.
*Scala default value:* ``true`` *; Python default value:* ``True``
*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.*
labelCol
Response variable column.
*Default value:* ``"label"``
*Also available on the trained model.*
lambdaMinRatio
Minimum lambda used in lambda search, specified as a ratio of lambda_max (the smallest lambda that drives all coefficients to zero). Default indicates: if the number of observations is greater than the number of variables, then lambda_min_ratio is set to 0.0001; if the number of observations is less than the number of variables, then lambda_min_ratio is set to 0.01.
*Default value:* ``-1.0``
*Also available on the trained model.*
lambdaSearch
Use lambda search starting at lambda max, given lambda is then interpreted as lambda min.
*Scala default value:* ``false`` *; Python default value:* ``False``
*Also available on the trained model.*
lambdaValue
Regularization strength.
*Scala default value:* ``null`` *; Python default value:* ``None``
*Also available on the trained model.*
link
Link function. Possible values are ``"family_default"``, ``"identity"``, ``"logit"``, ``"log"``, ``"inverse"``, ``"tweedie"``, ``"multinomial"``, ``"ologit"``, ``"oprobit"``, ``"ologlog"``.
*Default value:* ``"family_default"``
*Also available on the trained model.*
maxActivePredictors
Maximum number of active predictors during computation. Use as a stopping criterion to prevent expensive model building with many predictors. Default indicates: If the IRLSM solver is used, the value of max_active_predictors is set to 5000 otherwise it is set to 100000000.
*Default value:* ``-1``
*Also available on the trained model.*
maxAfterBalanceSize
Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.
*Scala default value:* ``5.0f`` *; Python default value:* ``5.0``
*Also available on the trained model.*
maxConfusionMatrixSize
[Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs.
*Default value:* ``20``
*Also available on the trained model.*
maxIterations
Maximum number of iterations.
*Default value:* ``-1``
*Also available on the trained model.*
maxIterationsDispersion
Control the maximum number of iterations in the dispersion parameter estimation loop using maximum likelihood.
*Default value:* ``3000``
*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.*
missingValuesHandling
Handling of missing values. Either MeanImputation, Skip or PlugValues. Possible values are ``"MeanImputation"``, ``"PlugValues"``, ``"Skip"``.
*Default value:* ``"MeanImputation"``
*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``
nfolds
Number of folds for K-fold cross-validation (0 to disable or >= 2).
*Default value:* ``0``
*Also available on the trained model.*
nlambdas
Number of lambdas to be used in a search. Default indicates: If alpha is zero, with lambda search set to True, the value of nlamdas is set to 30 (fewer lambdas are needed for ridge regression) otherwise it is set to 100.
*Default value:* ``-1``
*Also available on the trained model.*
nonNegative
Restrict coefficients (not intercept) to be non-negative.
*Scala default value:* ``false`` *; Python default value:* ``False``
*Also available on the trained model.*
objReg
Likelihood divider in objective value computation, default (of -1.0) will set it to 1/nobs.
*Default value:* ``-1.0``
*Also available on the trained model.*
objectiveEpsilon
Converge if objective value changes less than this. Default (of -1.0) indicates: If lambda_search is set to True the value of objective_epsilon is set to .0001. If the lambda_search is set to False and lambda is equal to zero, the value of objective_epsilon is set to .000001, for any other value of lambda the default value of objective_epsilon is set to .0001.
*Default value:* ``-1.0``
*Also available on the trained model.*
offsetCol
Offset column. This will be added to the combination of columns before applying the link function.
*Scala default value:* ``null`` *; Python default value:* ``None``
*Also available on the trained model.*
predictionCol
Prediction column name
*Default value:* ``"prediction"``
*Also available on the trained model.*
prior
Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality.
*Default value:* ``-1.0``
*Also available on the trained model.*
randomFamily
Random Component Family array. One for each random component. Only support gaussian for now. Possible values are ``"AUTO"``, ``"gaussian"``, ``"binomial"``, ``"fractionalbinomial"``, ``"quasibinomial"``, ``"poisson"``, ``"gamma"``, ``"multinomial"``, ``"tweedie"``, ``"ordinal"``, ``"negativebinomial"``.
*Scala default value:* ``null`` *; Python default value:* ``None``
*Also available on the trained model.*
randomLink
Link function array for random component in HGLM. Possible values are ``"family_default"``, ``"identity"``, ``"logit"``, ``"log"``, ``"inverse"``, ``"tweedie"``, ``"multinomial"``, ``"ologit"``, ``"oprobit"``, ``"ologlog"``.
*Scala default value:* ``null`` *; Python default value:* ``None``
*Also available on the trained model.*
removeCollinearCols
In case of linearly dependent columns, remove some of the dependent columns.
*Scala default value:* ``false`` *; Python default value:* ``False``
*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.*
scoreIterationInterval
Perform scoring for every score_iteration_interval iterations.
*Default value:* ``-1``
*Also available on the trained model.*
seed
Seed for pseudo random number generator (if applicable).
*Scala default value:* ``-1L`` *; Python default value:* ``-1``
*Also available on the trained model.*
solver
AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns. Possible values are ``"AUTO"``, ``"IRLSM"``, ``"L_BFGS"``, ``"COORDINATE_DESCENT_NAIVE"``, ``"COORDINATE_DESCENT"``, ``"GRADIENT_DESCENT_LH"``, ``"GRADIENT_DESCENT_SQERR"``.
*Default value:* ``"AUTO"``
*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 numeric columns to have zero mean and unit variance.
*Scala default value:* ``true`` *; Python default value:* ``True``
*Also available on the trained model.*
startval
double array to initialize fixed and random coefficients for HGLM, coefficients for GLM.
*Scala default value:* ``null`` *; Python default value:* ``None``
*Also available on the trained model.*
stoppingMetric
Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Possible values are ``"AUTO"``, ``"deviance"``, ``"logloss"``, ``"MSE"``, ``"RMSE"``, ``"MAE"``, ``"RMSLE"``, ``"AUC"``, ``"AUCPR"``, ``"lift_top_group"``, ``"misclassification"``, ``"mean_per_class_error"``, ``"anomaly_score"``, ``"custom"``, ``"custom_increasing"``.
*Default value:* ``"AUTO"``
*Also available on the trained model.*
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).
*Default value:* ``0``
*Also available on the trained model.*
stoppingTolerance
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much).
*Default value:* ``0.001``
*Also available on the trained model.*
theta
Theta.
*Scala default value:* ``1.0e-10`` *; Python default value:* ``1.0E-10``
*Also available on the trained model.*
tweedieEpsilon
In estimating tweedie dispersion parameter using maximum likelihood, this is used to choose the lower and upper indices in the approximating of the infinite series summation.
*Scala default value:* ``8.0e-17`` *; Python default value:* ``8.0E-17``
*Also available on the trained model.*
tweedieLinkPower
Tweedie link power.
*Default value:* ``1.0``
*Also available on the trained model.*
tweedieVariancePower
Tweedie variance power.
*Default value:* ``0.0``
*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. The parameter is not serializable!
*Scala default value:* ``null`` *; Python default value:* ``None``
weightCol
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. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
*Scala default value:* ``null`` *; Python default value:* ``None``
*Also available on the trained model.*
withContributions
Enables or disables generating a sub-column of detailedPredictionCol containing Shapley values of original features.
*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.*