FastTreeTweedieTrainer Class


The IEstimator<TTransformer> for training a decision tree regression model using Tweedie loss function. This trainer is a generalization of Poisson, compound Poisson, and gamma regression.

public sealed class FastTreeTweedieTrainer : Microsoft.ML.Trainers.FastTree.BoostingFastTreeTrainerBase<Microsoft.ML.Trainers.FastTree.FastTreeTweedieTrainer.Options,Microsoft.ML.Data.RegressionPredictionTransformer<Microsoft.ML.Trainers.FastTree.FastTreeTweedieModelParameters>,Microsoft.ML.Trainers.FastTree.FastTreeTweedieModelParameters>
type FastTreeTweedieTrainer = class
    inherit BoostingFastTreeTrainerBase<FastTreeTweedieTrainer.Options, RegressionPredictionTransformer<FastTreeTweedieModelParameters>, FastTreeTweedieModelParameters>
Public NotInheritable Class FastTreeTweedieTrainer
Inherits BoostingFastTreeTrainerBase(Of FastTreeTweedieTrainer.Options, RegressionPredictionTransformer(Of FastTreeTweedieModelParameters), FastTreeTweedieModelParameters)


To create this trainer, use FastTreeTweedie or FastTreeTweedie(Options).

Input and Output Columns

The input label column data must be Single. The input features column data must be a known-sized vector of Single.

This trainer outputs the following columns:

Output Column Name Column Type Description
Score Single The unbounded score that was predicted by the model.

Trainer Characteristics

Machine learning task Regression
Is normalization required? No
Is caching required? No
Required NuGet in addition to Microsoft.ML Microsoft.ML.FastTree
Exportable to ONNX Yes

Training Algorithm Details

The Tweedie boosting model follows the mathematics established in Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models from Yang, Quan, and Zou. For an introduction to Gradient Boosting, and more information, see: Wikipedia: Gradient boosting(Gradient tree boosting) or Greedy function approximation: A gradient boosting machine.

Check the See Also section for links to usage examples.



The feature column that the trainer expects.

(Inherited from TrainerEstimatorBase<TTransformer,TModel>)

The optional groupID column that the ranking trainers expects.

(Inherited from TrainerEstimatorBaseWithGroupId<TTransformer,TModel>)

The label column that the trainer expects. Can be null, which indicates that label is not used for training.

(Inherited from TrainerEstimatorBase<TTransformer,TModel>)

The weight column that the trainer expects. Can be null, which indicates that weight is not used for training.

(Inherited from TrainerEstimatorBase<TTransformer,TModel>)


Info (Inherited from FastTreeTrainerBase<TOptions,TTransformer,TModel>)



Trains and returns a ITransformer.

(Inherited from TrainerEstimatorBase<TTransformer,TModel>)
Fit(IDataView, IDataView)

Trains a FastTreeTweedieTrainer using both training and validation data, returns a RegressionPredictionTransformer<TModel>.

GetOutputSchema(SchemaShape) (Inherited from TrainerEstimatorBase<TTransformer,TModel>)

Extension Methods

AppendCacheCheckpoint<TTrans>(IEstimator<TTrans>, IHostEnvironment)

Append a 'caching checkpoint' to the estimator chain. This will ensure that the downstream estimators will be trained against cached data. It is helpful to have a caching checkpoint before trainers that take multiple data passes.

WithOnFitDelegate<TTransformer>(IEstimator<TTransformer>, Action<TTransformer>)

Given an estimator, return a wrapping object that will call a delegate once Fit(IDataView) is called. It is often important for an estimator to return information about what was fit, which is why the Fit(IDataView) method returns a specifically typed object, rather than just a general ITransformer. However, at the same time, IEstimator<TTransformer> are often formed into pipelines with many objects, so we may need to build a chain of estimators via EstimatorChain<TLastTransformer> where the estimator for which we want to get the transformer is buried somewhere in this chain. For that scenario, we can through this method attach a delegate that will be called once fit is called.

Applies to

See also