H2O XGBoost

H2O XGBoost

Input

It takes in a two DataFrame as input, one is for train and other one is for validation.

Type

ml-estimator

Class

fire.nodes.h2o.NodeH2OXGBoost

Fields

Name	Title	Description
isResponseIsCategorical	Is Response Column Categorical	Specify a response column type(numeric or categorical). Separates the Classification and Regression
labelCol	Label Column	Response variable column.
featuresCols	Feature Columns	Features to be used for Modelling
path	Path	Save Confusion Matrix to Path
columnsToCategorical	Columns to Categorical	Columns to be Categorical encoded
seed	Seed	Seed for pseudo random number generator (if applicable).
splitRatio	Split Ratio	Split Ratio
nfolds	Number of Folds	Number of folds for K-fold cross-validation (0 to disable or >= 2).
ntrees	Number of Trees	Number of trees.
maxDepth	Max Depth	Maximum tree depth (0 for unlimited).
minRows	Min Rows	Fewest allowed (weighted) observations in a leaf.
maxBins	Max Bins	For tree_method=hist only: maximum number of bins.
maxLeaves	Max Leaves	For tree_method=hist only: maximum number of leaves.
treeMethod	Tree Method	Tree method.
growPolicy	Grow Policy	Grow policy.
booster	Booster	Booster
eta	Eta	(same as learn_rate) Learning rate (from 0.0 to 1.0).
sampleRate	Sample Rate	(same as subsample) Row sample rate per tree (from 0.0 to 1.0)..
categoricalEncoding	Categorical Encoding	Specify one of the various encoding schemes for handling categorical features
ignoreConstCols	Ignore Const Columns	Ignore constant columns.
scoreEachIteration	Score Each Iteration	Whether to score during each iteration of model training.
stoppingRounds	Stopping Rounds	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	Max Runtime Secs	his argument specifies the maximum time that the AutoML process will run for. If both max_runtime_secs and max_models are specified, then the AutoML run will stop as soon as it hits either of these limits. If neither max_runtime_secs nor max_models are specified, then max_runtime_secs defaults to 3600 seconds (1 hour).
stoppingMetric	Stopping Metric	Metric to use for early stopping (AUTO: logloss for classification, deviance for regression)
stoppingTolerance	Stopping Tolerance	Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
gainsliftBins	Gains Lift Bins	Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
withContributions	With Contributions	Enables or disables generating a sub-column of detailedPredictionCol containing Shapley values.
advanced	Advanced
convertUnknownCategoricalLevelsToNa	Convert Unknown Categorical Levels to NA	If set to ‘true’, the model converts unknown categorical levels to NA during making predictions.
withLeafNodeAssignments	With Node Assignments	Enables or disables computation of leaf node assignments.
withStageResults	With Stage Results	Enables or disables computation of stage results.
minChildWeight	Min Child Weight	(same as min_rows) Fewest allowed (weighted) observations in a leaf.
learnRate	Learn Rate	(Same as eta) Learning rate (from 0.0 to 1.0).
subsample	Sample Rate	(same as sample_rate) Row sample rate per tree (from 0.0 to 1.0).
colSampleRate	Column Sample Rate	Column sample rate(from 0.0 to 1.0).
colSampleByLevel	Column Sample By Level	(same as col_sample_rate) Column sample rate (from 0.0 to 1.0).
colSampleRatePerTree	Column Sample Rate Per Tree	(same as colsample_bytree) Column sample rate per tree (from 0.0 to 1.0).
colSampleByTree	Column Sample By Tree	(same as col_sample_rate_per_tree) Column sample rate per tree (from 0.0 to 1.0).
colSampleByNode	Column Sample By Node	Column sample rate per tree node (from 0.0 to 1.0).
maxAbsLeafnodePred	Max Absolute Leaf Node Prediction	(same as max_delta_step) Maximum absolute value of a leaf node prediction.
maxDeltaStep	Max Delta Step	(same as max_abs_leafnode_pred) Maximum absolute value of a leaf node prediction.
scoreTreeInterval	Score Tree Interval	Score the model after every so many trees. Disabled if set to 0.
minSplitImprovement	Minimum Split Improvement
gamma	Gamma
nthreads	Number of Trees	Number of parallel threads that can be used to run XGBoost. Cannot exceed H2O cluster limits (-nthreads parameter). Defaults to maximum available.
buildTreeOneNode	Build tree one node	Enables to run on a single node
calibrateModel	Calibrate Model	Use Platt Scaling to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities.
regLambda	Reg Lambda	L2 regularization.
regAlpha	Reg Alpha	L1 regularization.
quietMode	Quiet mode	Enable quiet mode for less output to standard output.
sampleType	Sample Type	For booster=dart only: sample_type
normalizeType	Normalize Type	For booster=dart only: normalize_type
rateDrop	Rate Drop	For booster=dart only: rate_drop (0..1).
oneDrop	One Drop	For booster=dart only: one_drop.
skipDrop	Skip Drop	For booster=dart only: skip_drop (0..1).
dmatrixType	Dmatrix Type	Type of DMatrix. For sparse, NAs and 0 are treated equally.
scalePosWeight	Scaled Pos Weight	Controls the effect of observations with positive labels in relation to the observations with negative labels on gradient calculation. Useful for imbalanced problems.
keepCrossValidationModels	Keep Cross Validation Models	Whether to keep the cross-validated models. Keeping cross-validation models may consume significantly more memory in the H2O cluster.
keepCrossValidationPredictions	Keep Cross Validation Predictions	Whether to keep the predictions of the cross-validation predictions. This needs to be set to TRUE if running the same AutoML object for repeated runs because CV predictions are required to build additional Stacked Ensemble models in AutoML.
keepCrossValidationFoldAssignment	Keep Cross Validation Fold Assignment	Whether to keep cross-validation assignments.
distribution	Distribution	Distribution function.)
tweediePower	Tweedie Power	Tweedie power for Tweedie regression, must be between 1 and 2.
quantileAlpha	Quantile Alhpa	Desired quantile for Quantile regression, must be between 0 and 1.
huberAlpha	Huber Alpha	Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
weightCol	Weight Column	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.
offsetCol	Offset Column	Offset column. This will be added to the combination of columns before applying the link function.
foldCol	Fold Column	Column with cross-validation fold index assignment per observation.
foldAssignment	Fold Assignment	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.
aucType	AUC Type	Set default multinomial AUC type.
confusionMatrix	Confusion Matrix
output_confusion_matrix_chart	Output Confusion Matrix Chart	whether to display confusion matrix chart.
cm_chart_title	Confusion Matrix Chart Title	Title name to display in Confusion Matrix Chart
cm_chart_description	Confusion Matrix Chart Description	Description to display in Confusion Matrix CHart
confusionMatrixTargetLegend	Confusion Matrix Target Legend	Legend name to display for Target in Confusion Matrix
confusionMatrixPredictedLabelLegend	Confusion Matrix PredictedLabel Legend	Legend name to display for Predicted Label in Confusion Matrix
confusionMatrixCountLegend	Confusion Matrix Count Legend	Legend name to display for Count in Confusion Matrix
Description	Confusion Matrix Description
confusionMatrixRowDescription	Confusion Matrix Outcome description	One can provide the business details of the outcome of the confusion matrix rows
ROC Curve	ROC Curve
output_roc_curve	Output ROC Curve	whether to display confusion matrix chart.
roc_title	ROC Curve Chart Title	Title name to display in ROC Curve Chart
roc_description	ROC Curve Chart Description	Add Description for ROC Curve Chart
xlabel	X Label	X label
ylabel	Y Label	Y Label
Grid Search	Grid Search
paramKeys	Param Name	Param Names. eg: maxDepth ,learnRate, nTrees
paramValues	Param Value	Param Values. eg: 4,5,6
gridStrategy	Grid Search Strategy	Strategy to use for model hyperparameter search. Cartesian does exhaustive search; RandomDiscrete searches randomly within given time or model limits.
gridMaxModels	Grid Max Models	Maximum number of models to build in the grid search (0 for unlimited).
gridMaxRuntimeSecs	Grid Max Runtime Seconds	Maximum runtime in seconds for the grid search (0 for unlimited).
gridStoppingRounds	Grid Stopping Rounds	Early stopping based on convergence of the metric during grid search (0 to disable).
gridStoppingTolerance	Grid Stopping Tolerance	Tolerance for metric-based stopping criterion during grid search.
gridStoppingMetric	Grid Stopping Metric	Metric to use for early stopping during grid search (AUTO: logloss for classification, deviance for regression).
gridParallelism	Grid Parallelism	Level of parallelism to use when building models in the grid.
gridSelectBestModelBy	Grid Select Best Model By	Metric used to select the best model from the grid.