FastTreeTweedieTrainer Class
Definition
Important
Some information relates to prerelease product that may be substantially modified before it’s released. Microsoft makes no warranties, express or implied, with respect to the information provided here.
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)- Inheritance
Remarks
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.
Fields
| FeatureColumn |
The feature column that the trainer expects. (Inherited from TrainerEstimatorBase<TTransformer,TModel>) |
| GroupIdColumn |
The optional groupID column that the ranking trainers expects. (Inherited from TrainerEstimatorBaseWithGroupId<TTransformer,TModel>) |
| LabelColumn |
The label column that the trainer expects. Can be |
| WeightColumn |
The weight column that the trainer expects. Can be |
Properties
| Info | (Inherited from FastTreeTrainerBase<TOptions,TTransformer,TModel>) |
Methods
| Fit(IDataView, IDataView) |
Trains a FastTreeTweedieTrainer using both training and validation data, returns a RegressionPredictionTransformer<TModel>. |
| Fit(IDataView) |
Trains and returns a ITransformer. (Inherited from TrainerEstimatorBase<TTransformer,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. |