.. _parameters_H2OXGBoost: Parameters of H2OXGBoost ------------------------ Affected Classes ################ - ``ai.h2o.sparkling.ml.algos.H2OXGBoost`` - ``ai.h2o.sparkling.ml.algos.classification.H2OXGBoostClassifier`` - ``ai.h2o.sparkling.ml.algos.regression.H2OXGBoostRegressor`` Parameters ########## - *Each parameter has also a corresponding getter and setter method.* *(E.g.:* ``label`` *->* ``getLabel()`` *,* ``setLabel(...)`` *)* calibrationDataFrame Calibration frame for Platt Scaling. To enable usage of the data frame, set the parameter calibrateModel to True. *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.* monotoneConstraints A key must correspond to a feature name and value could be 1 or -1 *Scala default value:* ``Map()`` *; Python default value:* ``{}`` *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.* backend Backend. By default (auto), a GPU is used if available. Possible values are ``"auto"``, ``"gpu"``, ``"cpu"``. *Default value:* ``"auto"`` *Also available on the trained model.* booster Booster type. Possible values are ``"gbtree"``, ``"gblinear"``, ``"dart"``. *Default value:* ``"gbtree"`` *Also available on the trained model.* buildTreeOneNode Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets. *Scala default value:* ``false`` *; Python default value:* ``False`` *Also available on the trained model.* calibrateModel Use Platt Scaling to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities. *Scala default value:* ``false`` *; Python default value:* ``False`` *Also available on the trained model.* 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.* colSampleByLevel (same as col_sample_rate) Column sample rate (from 0.0 to 1.0). *Default value:* ``1.0`` *Also available on the trained model.* colSampleByNode Column sample rate per tree node (from 0.0 to 1.0). *Default value:* ``1.0`` *Also available on the trained model.* colSampleByTree (same as col_sample_rate_per_tree) Column sample rate per tree (from 0.0 to 1.0). *Default value:* ``1.0`` *Also available on the trained model.* colSampleRate (same as colsample_bylevel) Column sample rate (from 0.0 to 1.0). *Default value:* ``1.0`` *Also available on the trained model.* colSampleRatePerTree (same as colsample_bytree) Column sample rate per tree (from 0.0 to 1.0). *Default value:* ``1.0`` *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.* 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.* distribution Distribution function. Possible values are ``"AUTO"``, ``"bernoulli"``, ``"quasibinomial"``, ``"modified_huber"``, ``"multinomial"``, ``"ordinal"``, ``"gaussian"``, ``"poisson"``, ``"gamma"``, ``"tweedie"``, ``"huber"``, ``"laplace"``, ``"quantile"``, ``"fractionalbinomial"``, ``"negativebinomial"``, ``"custom"``. *Default value:* ``"AUTO"`` *Also available on the trained model.* dmatrixType Type of DMatrix. For sparse, NAs and 0 are treated equally. Possible values are ``"auto"``, ``"dense"``, ``"sparse"``. *Default value:* ``"auto"`` *Also available on the trained model.* eta (same as learn_rate) Learning rate (from 0.0 to 1.0). *Default value:* ``0.3`` *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.* gainsliftBins Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. *Default value:* ``-1`` *Also available on the trained model.* gamma (same as min_split_improvement) Minimum relative improvement in squared error reduction for a split to happen. *Scala default value:* ``0.0f`` *; Python default value:* ``0.0`` *Also available on the trained model.* gpuId Which GPU(s) to use. . *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* growPolicy Grow policy - depthwise is standard GBM, lossguide is LightGBM. Possible values are ``"depthwise"``, ``"lossguide"``. *Default value:* ``"depthwise"`` *Also available on the trained model.* ignoreConstCols Ignore constant columns. *Scala default value:* ``true`` *; Python default value:* ``True`` *Also available on the trained model.* interactionConstraints A set of allowed column interactions. *Scala default value:* ``null`` *; Python default value:* ``None`` *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.* learnRate (same as eta) Learning rate (from 0.0 to 1.0). *Default value:* ``0.3`` *Also available on the trained model.* maxAbsLeafnodePred (same as max_delta_step) Maximum absolute value of a leaf node prediction. *Scala default value:* ``0.0f`` *; Python default value:* ``0.0`` *Also available on the trained model.* maxBins For tree_method=hist only: maximum number of bins. *Default value:* ``256`` *Also available on the trained model.* maxDeltaStep (same as max_abs_leafnode_pred) Maximum absolute value of a leaf node prediction. *Scala default value:* ``0.0f`` *; Python default value:* ``0.0`` *Also available on the trained model.* maxDepth Maximum tree depth (0 for unlimited). *Default value:* ``6`` *Also available on the trained model.* maxLeaves For tree_method=hist only: maximum number of leaves. *Default value:* ``0`` *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.* minChildWeight (same as min_rows) Fewest allowed (weighted) observations in a leaf. *Default value:* ``1.0`` *Also available on the trained model.* minRows (same as min_child_weight) Fewest allowed (weighted) observations in a leaf. *Default value:* ``1.0`` *Also available on the trained model.* minSplitImprovement (same as gamma) Minimum relative improvement in squared error reduction for a split to happen. *Scala default value:* ``0.0f`` *; Python 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.* normalizeType For booster=dart only: normalize_type. Possible values are ``"tree"``, ``"forest"``. *Default value:* ``"tree"`` *Also available on the trained model.* nthread Number of parallel threads that can be used to run XGBoost. Cannot exceed H2O cluster limits (-nthreads parameter). Defaults to maximum available. *Default value:* ``-1`` *Also available on the trained model.* ntrees (same as n_estimators) Number of trees. *Default value:* ``50`` *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.* oneDrop For booster=dart only: one_drop. *Scala default value:* ``false`` *; Python default value:* ``False`` *Also available on the trained model.* predictionCol Prediction column name *Default value:* ``"prediction"`` *Also available on the trained model.* quietMode Enable quiet mode. *Scala default value:* ``true`` *; Python default value:* ``True`` *Also available on the trained model.* rateDrop For booster=dart only: rate_drop (0..1). *Scala default value:* ``0.0f`` *; Python default value:* ``0.0`` *Also available on the trained model.* regAlpha L1 regularization. *Scala default value:* ``0.0f`` *; Python default value:* ``0.0`` *Also available on the trained model.* regLambda L2 regularization. *Scala default value:* ``1.0f`` *; Python default value:* ``1.0`` *Also available on the trained model.* sampleRate (same as subsample) Row sample rate per tree (from 0.0 to 1.0). *Default value:* ``1.0`` *Also available on the trained model.* sampleType For booster=dart only: sample_type. Possible values are ``"uniform"``, ``"weighted"``. *Default value:* ``"uniform"`` *Also available on the trained model.* saveMatrixDirectory Directory where to save matrices passed to XGBoost library. Useful for debugging. *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* scalePosWeight Controls the effect of observations with positive labels in relation to the observations with negative labels on gradient calculation. Useful for imbalanced problems. *Scala default value:* ``1.0f`` *; Python default value:* ``1.0`` *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.* scoreTreeInterval Score the model after every so many trees. Disabled if set to 0. *Default value:* ``0`` *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.* skipDrop For booster=dart only: skip_drop (0..1). *Scala default value:* ``0.0f`` *; Python default value:* ``0.0`` *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`` stoppingMetric Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_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.* subsample (same as sample_rate) Row sample rate per tree (from 0.0 to 1.0). *Default value:* ``1.0`` *Also available on the trained model.* treeMethod Tree method. Possible values are ``"auto"``, ``"exact"``, ``"approx"``, ``"hist"``. *Default value:* ``"auto"`` *Also available on the trained model.* tweediePower Tweedie power for Tweedie regression, must be between 1 and 2. *Default value:* ``1.5`` *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. *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.*