5 instantiations of FastTreeTweedieTrainer
Microsoft.ML.FastTree (5)
FastTreeArguments.cs (1)
210ITrainer IComponentFactory<ITrainer>.CreateComponent(IHostEnvironment env) => new FastTreeTweedieTrainer(env, this);
FastTreeTweedie.cs (1)
578() => new FastTreeTweedieTrainer(host, input),
TreeEnsembleFeaturizationEstimator.cs (1)
459var trainer = new FastTreeTweedieTrainer(Env, _trainerOptions);
TreeTrainersCatalog.cs (2)
311return new FastTreeTweedieTrainer(env, labelColumnName, featureColumnName, exampleWeightColumnName, numberOfLeaves, numberOfTrees, minimumExampleCountPerLeaf, learningRate); 333return new FastTreeTweedieTrainer(env, options);
41 references to FastTreeTweedieTrainer
Microsoft.ML.AutoML (3)
API\RegressionExperiment.cs (1)
91/// See <see cref="FastTreeTweedieTrainer"/>.
SweepableEstimator\Estimators\FastTree.cs (1)
59var option = new FastTreeTweedieTrainer.Options()
TrainerExtensions\RegressionTrainerExtensions.cs (1)
69var options = TrainerExtensionUtil.CreateOptions<FastTreeTweedieTrainer.Options>(sweepParams, columnInfo.LabelColumnName);
Microsoft.ML.FastTree (29)
FastTreeArguments.cs (2)
15[assembly: EntryPointModule(typeof(FastTreeTweedieTrainer.Options))] 159/// Options for the <see cref="FastTreeTweedieTrainer"/> as used in
FastTreeTweedie.cs (17)
17[assembly: LoadableClass(FastTreeTweedieTrainer.Summary, typeof(FastTreeTweedieTrainer), typeof(FastTreeTweedieTrainer.Options), 19FastTreeTweedieTrainer.UserNameValue, 20FastTreeTweedieTrainer.LoadNameValue, 21FastTreeTweedieTrainer.ShortName)] 61/// <seealso cref="TreeExtensions.FastTreeTweedie(RegressionCatalog.RegressionTrainers, FastTreeTweedieTrainer.Options)"/> 64: BoostingFastTreeTrainerBase<FastTreeTweedieTrainer.Options, RegressionPredictionTransformer<FastTreeTweedieModelParameters>, FastTreeTweedieModelParameters> 80/// Initializes a new instance of <see cref="FastTreeTweedieTrainer"/> 107/// Initializes a new instance of <see cref="FastTreeTweedieTrainer"/> by using the <see cref="Options"/> class. 354/// Trains a <see cref="FastTreeTweedieTrainer"/> using both training and validation data, returns 485/// Model parameters for <see cref="FastTreeTweedieTrainer"/>. 567Desc = FastTreeTweedieTrainer.Summary, 568UserName = FastTreeTweedieTrainer.UserNameValue, 569ShortName = FastTreeTweedieTrainer.ShortName)] 570public static CommonOutputs.RegressionOutput TrainTweedieRegression(IHostEnvironment env, FastTreeTweedieTrainer.Options input) 577return TrainerEntryPointsUtils.Train<FastTreeTweedieTrainer.Options, CommonOutputs.RegressionOutput>(host, input,
TreeEnsembleFeaturizationEstimator.cs (4)
438private readonly FastTreeTweedieTrainer.Options _trainerOptions; 446/// The configuration of <see cref="FastTreeTweedieTrainer"/> used to train the underlying <see cref="TreeEnsembleModelParameters"/>. 448public FastTreeTweedieTrainer.Options TrainerOptions; 459var trainer = new FastTreeTweedieTrainer(Env, _trainerOptions);
TreeTrainersCatalog.cs (6)
283/// Create <see cref="FastTreeTweedieTrainer"/>, which predicts a target using a decision tree regression model. 300public static FastTreeTweedieTrainer FastTreeTweedie(this RegressionCatalog.RegressionTrainers catalog, 315/// Create <see cref="FastTreeTweedieTrainer"/> using advanced options, which predicts a target using a decision tree regression model. 326public static FastTreeTweedieTrainer FastTreeTweedie(this RegressionCatalog.RegressionTrainers catalog, 327FastTreeTweedieTrainer.Options options) 567/// Create <see cref="FastTreeTweedieFeaturizationEstimator"/>, which uses <see cref="FastTreeTweedieTrainer"/> to train <see cref="TreeEnsembleModelParameters"/> to create tree-based features.
Microsoft.ML.Samples (4)
Dynamic\Trainers\Regression\FastTreeTweedie.cs (1)
30var pipeline = mlContext.Regression.Trainers.FastTreeTweedie(
Dynamic\Trainers\Regression\FastTreeTweedieWithOptions.cs (2)
31var options = new FastTreeTweedieTrainer.Options 46var pipeline =
Dynamic\Transforms\TreeFeaturization\FastTreeTweedieFeaturizationWithOptions.cs (1)
46var trainerOptions = new FastTreeTweedieTrainer.Options
Microsoft.ML.Tests (5)
TrainerEstimators\TreeEnsembleFeaturizerTest.cs (1)
511var trainerOptions = new FastTreeTweedieTrainer.Options
TrainerEstimators\TreeEstimators.cs (4)
271var trainer = ML.Regression.Trainers.FastTreeTweedie( 272new FastTreeTweedieTrainer.Options 945var trainer = ML.Regression.Trainers.FastTreeTweedie( 946new FastTreeTweedieTrainer.Options { NumberOfTrees = 10, NumberOfThreads = 1, NumberOfLeaves = 5 });