Parameters of H2OGLM¶
Affected Classes¶
ai.h2o.sparkling.ml.algos.H2OGLMai.h2o.sparkling.ml.algos.classification.H2OGLMClassifierai.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:FalseAlso 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:NoneAlso available on the trained model.
- interactionPairs
A list of pairwise (first order) column interactions.
Scala default value:
null; Python default value:None- linearConstraints
Data frame used to specify linear constraints involving more than one coefficients in standard form.
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:NoneAlso 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:FalseAlso 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-4Also 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:FalseAlso available on the trained model.
- calcLike
if true, will return likelihood function value.
Scala default value:
false; Python default value:FalseAlso available on the trained model.
- checkpoint
Model checkpoint to resume training with.
Scala default value:
null; Python default value:NoneAlso 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:NoneAlso 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:FalseAlso 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.
Scala default value:
false; Python default value:FalseAlso available on the trained model.
- constraintAlpha
For constrained GLM only. It affects the setting of eta_k = eta_0/pow(c_0, alpha).
Default value:
0.1Also available on the trained model.
- constraintBeta
For constrained GLM only. It affects the setting of eta_k+1 = eta_k/pow(c_k, beta).
Default value:
0.9Also available on the trained model.
- constraintC0
For constrained GLM only. It affects the initial setting of epsilon_k = 1/c_0.
Default value:
10.0Also available on the trained model.
- constraintEta0
For constrained GLM only. It affects the setting of eta_k+1=eta_0/power(ck+1, alpha).
Default value:
0.1258925Also available on the trained model.
- constraintTau
For constrained GLM only. It affects the setting of c_k+1=tau*c_k.
Default value:
10.0Also 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:FalseAlso 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:FalseAlso available on the trained model.
- customMetricFunc
Reference to custom evaluation function, format: language:keyName=funcName.
Scala default value:
null; Python default value:NoneAlso 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-4Also 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:
0.5Also 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:TrueAlso available on the trained model.
- exportCheckpointsDir
Automatically export generated models to this directory.
Scala default value:
null; Python default value:NoneAlso 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:FalseAlso 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:TrueAlso 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:NoneAlso available on the trained model.
- gainsliftBins
Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
Default value:
-1Also 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:FalseAlso 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:FalseAlso 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:
-1.0Also available on the trained model.
- ignoreConstCols
Ignore constant columns.
Scala default value:
true; Python default value:TrueAlso 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:NoneAlso 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 value:
1.0Also available on the trained model.
- initOptimalGlm
If true, will initialize coefficients with values derived from GLM runs without linear constraints. Only available for linear constraints.
Scala default value:
false; Python default value:FalseAlso 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:NoneAlso available on the trained model.
- intercept
Include constant term in the model.
Scala default value:
true; Python default value:TrueAlso available on the trained model.
- keepBinaryModels
If set to true, all binary models created during execution of the
fitmethod 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:FalseAlso available on the trained model.
- keepCrossValidationModels
Whether to keep the cross-validation models.
Scala default value:
true; Python default value:TrueAlso available on the trained model.
- keepCrossValidationPredictions
Whether to keep the predictions of the cross-validation models.
Scala default value:
false; Python default value:FalseAlso 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:
-1.0Also 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:FalseAlso available on the trained model.
- lambdaValue
Regularization strength.
Scala default value:
null; Python default value:NoneAlso 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:
-1Also 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:5.0Also available on the trained model.
- maxConfusionMatrixSize
[Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs.
Default value:
20Also available on the trained model.
- maxIterations
Maximum number of iterations. Value should >=1. A value of 0 is only set when only the model coefficient names and model coefficient dimensions are needed.
Default value:
-1Also available on the trained model.
- maxIterationsDispersion
Control the maximum number of iterations in the dispersion parameter estimation loop using maximum likelihood.
Default value:
3000Also available on the trained model.
- maxRuntimeSecs
Maximum allowed runtime in seconds for model training. Use 0 to disable.
Default value:
0.0Also 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:
0Also 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:
-1Also available on the trained model.
- nonNegative
Restrict coefficients (not intercept) to be non-negative.
Scala default value:
false; Python default value:FalseAlso available on the trained model.
- objReg
Likelihood divider in objective value computation, default (of -1.0) will set it to 1/nobs.
Default value:
-1.0Also 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:
-1.0Also 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:NoneAlso 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:
-1.0Also 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:NoneAlso 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:NoneAlso available on the trained model.
- removeCollinearCols
In case of linearly dependent columns, remove the dependent columns.
Scala default value:
false; Python default value:FalseAlso available on the trained model.
- scoreEachIteration
Whether to score during each iteration of model training.
Scala default value:
false; Python default value:FalseAlso available on the trained model.
- scoreIterationInterval
Perform scoring for every score_iteration_interval iterations.
Default value:
-1Also available on the trained model.
- seed
Seed for pseudo random number generator (if applicable).
Scala default value:
-1L; Python default value:-1Also available on the trained model.
- separateLinearBeta
If true, will keep the beta constraints and linear constraints separate. After new coefficients are found, first beta constraints will be applied followed by the application of linear constraints. Note that the beta constraints in this case will not be part of the objective function. If false, will combine the beta and linear constraints.
Scala default value:
false; Python default value:FalseAlso 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:TrueAlso available on the trained model.
- startval
double array to initialize fixed and random coefficients for HGLM, coefficients for GLM. If standardize is true, the standardized coefficients should be used. Otherwise, use the regular coefficients.
Scala default value:
null; Python default value:NoneAlso 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","AUUC","ATE","ATT","ATC","qini","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:
0Also 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:
0.001Also available on the trained model.
- theta
Theta.
Scala default value:
1.0e-10; Python default value:1.0E-10Also 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-17Also available on the trained model.
- tweedieLinkPower
Tweedie link power.
Default value:
1.0Also available on the trained model.
- tweedieVariancePower
Tweedie variance power.
Default value:
0.0Also 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:NoneAlso 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:FalseAlso available on the trained model.
- withLeafNodeAssignments
Enables or disables computation of leaf node assignments.
Scala default value:
false; Python default value:FalseAlso available on the trained model.
- withStageResults
Enables or disables computation of stage results.
Scala default value:
false; Python default value:FalseAlso available on the trained model.