# FastTree (Boosted Trees) Tweedie Regression¶

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

**Type:** regressortrainer
**Aliases:** *FastTreeTweedieRegression, fttweedie*
**Namespace:** Microsoft.ML.Trainers.FastTree
**Assembly:** Microsoft.ML.FastTree.dll
**Microsoft Documentation:** FastTree (Boosted Trees) Tweedie Regression

**Description**

Trains gradient boosted decision trees to fit target values using a Tweedie loss function. This learner is a generalization of Poisson, compound Poisson, and gamma regression.

**Parameters**

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

allowEmptyTrees | allowempty | True | When a root split is impossible, allow training to proceed |

baggingSize | bag | 0 | Number of trees in each bag (0 for disabling bagging) |

baggingTrainFraction | bagfrac | 0.7 | Percentage of training examples used in each bag |

baselineAlphaRisk | basealpha | Baseline alpha for tradeoffs of risk (0 is normal training) | |

baselineScoresFormula | basescores | Freeform defining the scores that should be used as the baseline ranker | |

bestStepRankingRegressionTrees | bsr | False | Use best regression step trees? |

bias | 0 | Bias for calculating gradient for each feature bin for a categorical feature. | |

bundling | bundle | None | Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle. |

categoricalSplit | cat | False | Whether to do split based on multiple categorical feature values. |

compressEnsemble | cmp | False | Compress the tree Ensemble |

diskTranspose | dt | Whether to utilize the disk or the data’s native transposition facilities (where applicable) when performing the transpose | |

dropoutRate | tdrop | 0 | Dropout rate for tree regularization |

earlyStoppingMetrics | esmt | 0 | Early stopping metrics. (For regression, 1: L1, 2:L2; for ranking, 1:NDCG@1, 3:NDCG@3) |

earlyStoppingRule | esr | Early stopping rule. (Validation set (/valid) is required.) | |

enablePruning | pruning | False | Enable post-training pruning to avoid overfitting. (a validation set is required) |

entropyCoefficient | e | 0 | The entropy (regularization) coefficient between 0 and 1 |

executionTimes | et | False | Print execution time breakdown to stdout |

featureCompressionLevel | fcomp | 1 | The level of feature compression to use |

featureFirstUsePenalty | ffup | 0 | The feature first use penalty coefficient |

featureFlocks | flocks | True | Whether to collectivize features during dataset preparation to speed up training |

featureFraction | ff | 1 | The fraction of features (chosen randomly) to use on each iteration |

featureReusePenalty | frup | 0 | The feature re-use penalty (regularization) coefficient |

featureSelectSeed | r3 | 123 | The seed of the active feature selection |

filterZeroLambdas | fzl | False | Filter zero lambdas during training |

gainConfidenceLevel | gainconf | 0 | Tree fitting gain confidence requirement (should be in the range [0,1) ). |

getDerivativesSampleRate | sr | 1 | Sample each query 1 in k times in the GetDerivatives function |

histogramPoolSize | ps | -1 | The number of histograms in the pool (between 2 and numLeaves) |

index | 1.5 | 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. | |

learningRates | lr | 0.2 | The learning rate |

maxBins | mb | 255 | Maximum number of distinct values (bins) per feature |

maxCategoricalGroupsPerNode | mcg | 64 | 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 | maxcat | 64 | Maximum categorical split points to consider when splitting on a categorical feature. |

maxTreeOutput | mo | 100 | Upper bound on absolute value of single tree output |

maxTreesAfterCompression | cmpmax | -1 | Maximum Number of trees after compression |

minDocsForCategoricalSplit | mdo | 100 | Minimum categorical doc count in a bin to consider for a split. |

minDocsPercentageForCategoricalSplit | mdop | 0.001 | Minimum categorical docs percentage in a bin to consider for a split. |

minDocumentsInLeafs | mil | 10 | The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data |

minStepSize | minstep | 0 | Minimum line search step size |

numLeaves | nl | 20 | The max number of leaves in each regression tree |

numPostBracketSteps | lssteps | 0 | Number of post-bracket line search steps |

numThreads | t | The number of threads to use | |

numTrees | iter | 100 | Total number of decision trees to create in the ensemble |

optimizationAlgorithm | oa | GradientDescent | Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent) |

parallelTrainer | parag | Microsoft. ML. Trainers. FastTree. SingleTrainerFactory | Allows to choose Parallel FastTree Learning Algorithm |

positionDiscountFreeform | pdff | 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 | graph | False | Print metrics graph for the first test set |

printTrainValidGraph | graphtv | False | Print Train and Validation metrics in graph |

pruningThreshold | prth | 0.004 | The tolerance threshold for pruning |

pruningWindowSize | prws | 5 | The moving window size for pruning |

randomStart | rs | False | Training starts from random ordering (determined by /r1) |

rngSeed | r1 | 123 | The seed of the random number generator |

shrinkage | shrk | 1 | Shrinkage |

smoothing | s | 0 | Smoothing paramter for tree regularization |

softmaxTemperature | smtemp | 0 | The temperature of the randomized softmax distribution for choosing the feature |

sparsifyThreshold | sp | 0.7 | Sparsity level needed to use sparse feature representation |

splitFraction | sf | 1 | The fraction of features (chosen randomly) to use on each split |

testFrequency | tf | 2147483647 | Calculate metric values for train/valid/test every k rounds |

useLineSearch | ls | False | Should we use line search for a step size |

useTolerantPruning | prtol | False | Use window and tolerance for pruning |

writeLastEnsemble | hl | False | Write the last ensemble instead of the one determined by early stopping |