# Generalized Additive Model for Binary Classification¶

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

**Type:** binaryclassifiertrainer
**Aliases:** *BinaryClassificationGamTrainer, gam*
**Namespace:** Microsoft.ML.Trainers.FastTree
**Assembly:** Microsoft.ML.FastTree.dll
**Microsoft Documentation:** Generalized Additive Model for Binary Classification

**Description**

Trains a gradient boosted stump per feature, on all features simultaneously, to fit target values using least-squares. It mantains no interactions between features.

**Parameters**

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

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

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

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

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

learningRates | lr | 0.002 | The learning rate |

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

maxOutput | mo | ∞ | Upper bound on absolute value of single output |

minDocuments | mi | 10 | Minimum number of training instances required to form a partition |

numIterations | iter | 9500 | Total number of iterations over all features |

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

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

unbalancedSets | us | False | Should we use derivatives optimized for unbalanced sets |