# Fast Forest Regression¶

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

**Type:** regressortrainer
**Aliases:** *FastForestRegression, ffr*
**Namespace:** Microsoft.ML.Trainers.FastTree
**Assembly:** Microsoft.ML.FastTree.dll
**Microsoft Documentation:** Fast Forest Regression

**Description**

Trains a random forest to fit target values using least-squares.

**Parameters**

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

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

baggingSize | bag | 1 | Number of trees in each bag (0 for disabling bagging) |

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

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 | |

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 | 0.7 | 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 |

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

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

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. |

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 |

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

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

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

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

printTestGraph | graph | False | Print metrics graph for the first test set |

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

quantileSampleCount | qsc | 100 | Number of labels to be sampled from each leaf to make the distribtuion |

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

shuffleLabels | False | Shuffle the labels on every iteration. Useful probably only if using this tree as a tree leaf featurizer for multiclass. | |

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 | 0.7 | The fraction of features (chosen randomly) to use on each split |

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