# FastTreeTweedieTrainer.Arguments¶

The documentation is generated based on the sources available at dotnet/machinelearning and released under MIT License.

**Type:** argument
**Aliases:** *Microsoft.ML.Trainers.FastTree.FastTreeTweedieTrainer+Arguments*
**Namespace:** System
**Assembly:** Microsoft.ML.FastTree.dll
**Microsoft Documentation:** FastTreeTweedieTrainer.Arguments

**Description**

**Parameters**

Name | Short name | Default | Description |
---|---|---|---|

AllowEmptyTrees | ShortName = “allowempty,dummies” | True | HelpText = “When a root split is impossible, allow training to proceed” |

BaggingSize | ShortName = “bag” | 0 | HelpText = “Number of trees in each bag (0 for disabling bagging)” |

BaggingTrainFraction | ShortName = “bagfrac” | 0.7 | HelpText = “Percentage of training examples used in each bag” |

BaselineAlphaRisk | ShortName = “basealpha” | HelpText = “Baseline alpha for tradeoffs of risk (0 is normal training)” | |

BaselineScoresFormula | ShortName = “basescores” | HelpText = “Freeform defining the scores that should be used as the baseline ranker” | |

BestStepRankingRegressionTrees | ShortName = “bsr” | False | HelpText = “Use best regression step trees?” |

Bias | ShortName = “bias” | 0 | HelpText = “Bias for calculating gradient for each feature bin for a categorical feature.” |

Bundling | ShortName = “bundle” | None | HelpText = “Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.” |

Caching | ShortName = “cache” | Auto | HelpText = “Whether learner should cache input training data” |

CategoricalSplit | ShortName = “cat” | False | HelpText = “Whether to do split based on multiple categorical feature values.” |

CompressEnsemble | ShortName = “cmp” | False | HelpText = “Compress the tree Ensemble” |

DiskTranspose | ShortName = “dt” | HelpText = “Whether to utilize the disk or the data’s native transposition facilities (where applicable) when performing the transpose” | |

DropoutRate | ShortName = “tdrop” | 0 | HelpText = “Dropout rate for tree regularization” |

EarlyStoppingMetrics | ShortName = “esmt” | 0 | HelpText = “Early stopping metrics. (For regression, 1: L1, 2:L2; for ranking, 1:NDCG@1, 3:NDCG@3)” |

EarlyStoppingRule | ShortName = “esr” | HelpText = “Early stopping rule. (Validation set (/valid) is required.)” | |

EnablePruning | ShortName = “pruning” | False | HelpText = “Enable post-training pruning to avoid overfitting. (a validation set is required)” |

EntropyCoefficient | ShortName = “e” | 0 | HelpText = “The entropy (regularization) coefficient between 0 and 1” |

ExecutionTimes | ShortName = “et” | False | HelpText = “Print execution time breakdown to stdout” |

FeatureColumn | ShortName = “feat” | Features | HelpText = “Column to use for features” |

FeatureCompressionLevel | ShortName = “fcomp” | 1 | HelpText = “The level of feature compression to use” |

FeatureFirstUsePenalty | ShortName = “ffup” | 0 | HelpText = “The feature first use penalty coefficient” |

FeatureFlocks | ShortName = “flocks” | True | HelpText = “Whether to collectivize features during dataset preparation to speed up training” |

FeatureFraction | ShortName = “ff” | 1 | HelpText = “The fraction of features (chosen randomly) to use on each iteration” |

FeatureReusePenalty | ShortName = “frup” | 0 | HelpText = “The feature re-use penalty (regularization) coefficient” |

FeatureSelectSeed | ShortName = “r3” | 123 | HelpText = “The seed of the active feature selection” |

FilterZeroLambdas | ShortName = “fzl” | False | HelpText = “Filter zero lambdas during training” |

GainConfidenceLevel | ShortName = “gainconf” | 0 | HelpText = “Tree fitting gain confidence requirement (should be in the range [0,1) ).” |

GetDerivativesSampleRate | ShortName = “sr” | 1 | HelpText = “Sample each query 1 in k times in the GetDerivatives function” |

GroupIdColumn | ShortName = “groupId” | GroupId | HelpText = “Column to use for example groupId” |

HistogramPoolSize | ShortName = “ps” | -1 | HelpText = “The number of histograms in the pool (between 2 and numLeaves)” |

Index | 1.5 | HelpText = “Index parameter for the Tweedie distribution, in the range [1, 2]. 1 is Poisson loss, 2 is gamma loss, and intermediate values are compound Poisson loss.” | |

LabelColumn | ShortName = “lab” | Label | HelpText = “Column to use for labels” |

LearningRates | ShortName = “lr” | 0.2 | HelpText = “The learning rate” |

MaxBins | ShortName = “mb” | 255 | HelpText = “Maximum number of distinct values (bins) per feature” |

MaxCategoricalGroupsPerNode | ShortName = “mcg” | 64 | HelpText = “Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.” |

MaxCategoricalSplitPoints | ShortName = “maxcat” | 64 | HelpText = “Maximum categorical split points to consider when splitting on a categorical feature.” |

MaxTreeOutput | ShortName = “mo” | 100 | HelpText = “Upper bound on absolute value of single tree output” |

MaxTreesAfterCompression | ShortName = “cmpmax” | -1 | HelpText = “Maximum Number of trees after compression” |

MinDocsForCategoricalSplit | ShortName = “mdo” | 100 | HelpText = “Minimum categorical doc count in a bin to consider for a split.” |

MinDocsPercentageForCategoricalSplit | ShortName = “mdop” | 0.001 | HelpText = “Minimum categorical docs percentage in a bin to consider for a split.” |

MinDocumentsInLeafs | ShortName = “mil” | 10 | HelpText = “The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data” |

MinStepSize | ShortName = “minstep” | 0 | HelpText = “Minimum line search step size” |

NormalizeFeatures | ShortName = “norm” | Auto | HelpText = “Normalize option for the feature column” |

NumLeaves | ShortName = “nl” | 20 | HelpText = “The max number of leaves in each regression tree” |

NumPostBracketSteps | ShortName = “lssteps” | 0 | HelpText = “Number of post-bracket line search steps” |

NumThreads | ShortName = “t” | HelpText = “The number of threads to use” | |

NumTrees | ShortName = “iter” | 100 | HelpText = “Total number of decision trees to create in the ensemble” |

OptimizationAlgorithm | ShortName = “oa” | GradientDescent | HelpText = “Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent)” |

ParallelTrainer | ShortName = “parag” | Microsoft. ML. Trainers. FastTree. SingleTrainerFactory | HelpText = “Allows to choose Parallel FastTree Learning Algorithm” |

PositionDiscountFreeform | ShortName = “pdff” | HelpText = “The discount freeform which specifies the per position discounts of documents in a query (uses a single variable P for position where P=0 is first position)” | |

PrintTestGraph | ShortName = “graph” | False | HelpText = “Print metrics graph for the first test set” |

PrintTrainValidGraph | ShortName = “graphtv” | False | HelpText = “Print Train and Validation metrics in graph” |

PruningThreshold | ShortName = “prth” | 0.004 | HelpText = “The tolerance threshold for pruning” |

PruningWindowSize | ShortName = “prws” | 5 | HelpText = “The moving window size for pruning” |

RandomStart | ShortName = “rs” | False | HelpText = “Training starts from random ordering (determined by /r1)” |

RngSeed | ShortName = “r1” | 123 | HelpText = “The seed of the random number generator” |

Shrinkage | ShortName = “shrk” | 1 | HelpText = “Shrinkage” |

Smoothing | ShortName = “s” | 0 | HelpText = “Smoothing paramter for tree regularization” |

SoftmaxTemperature | ShortName = “smtemp” | 0 | HelpText = “The temperature of the randomized softmax distribution for choosing the feature” |

SparsifyThreshold | ShortName = “sp” | 0.7 | HelpText = “Sparsity level needed to use sparse feature representation” |

SplitFraction | ShortName = “sf” | 1 | HelpText = “The fraction of features (chosen randomly) to use on each split” |

TestFrequency | ShortName = “tf” | 2147483647 | HelpText = “Calculate metric values for train/valid/test every k rounds” |

TrainingData | ShortName = “data” | HelpText = “The data to be used for training” | |

UseLineSearch | ShortName = “ls” | False | HelpText = “Should we use line search for a step size” |

UseTolerantPruning | ShortName = “prtol” | False | HelpText = “Use window and tolerance for pruning” |

WeightColumn | ShortName = “weight” | Weight | HelpText = “Column to use for example weight” |

WriteLastEnsemble | ShortName = “hl” | False | HelpText = “Write the last ensemble instead of the one determined by early stopping” |