4 instantiations of LightGbmRegressionTrainer
Microsoft.ML.LightGbm (4)
LightGbmCatalog.cs (3)
48return new LightGbmRegressionTrainer(env, labelColumnName, featureColumnName, exampleWeightColumnName, numberOfLeaves, minimumExampleCountPerLeaf, learningRate, numberOfIterations); 68return new LightGbmRegressionTrainer(env, options); 84return new LightGbmRegressionTrainer(env, lightGbmModel, featureColumnName);
LightGbmRegressionTrainer.cs (1)
282() => new LightGbmRegressionTrainer(host, input),
38 references to LightGbmRegressionTrainer
Microsoft.ML.AutoML (4)
API\RegressionExperiment.cs (1)
96/// See <see cref="LightGbmRegressionTrainer"/>.
SweepableEstimator\Estimators\LightGbm.cs (1)
69var option = new LightGbmRegressionTrainer.Options()
TrainerExtensions\RegressionTrainerExtensions.cs (2)
91LightGbmRegressionTrainer.Options options = TrainerExtensionUtil.CreateLightGbmOptions<LightGbmRegressionTrainer.Options, float, RegressionPredictionTransformer<LightGbmRegressionModelParameters>, LightGbmRegressionModelParameters>(sweepParams, columnInfo);
Microsoft.ML.LightGbm (25)
LightGbmCatalog.cs (7)
20/// Create <see cref="LightGbmRegressionTrainer"/>, which predicts a target using a gradient boosting decision tree regression model. 37public static LightGbmRegressionTrainer LightGbm(this RegressionCatalog.RegressionTrainers catalog, 52/// Create <see cref="LightGbmRegressionTrainer"/> using advanced options, which predicts a target using a gradient boosting decision tree regression model. 63public static LightGbmRegressionTrainer LightGbm(this RegressionCatalog.RegressionTrainers catalog, 64LightGbmRegressionTrainer.Options options) 72/// Create <see cref="LightGbmRegressionTrainer"/> from a pre-trained LightGBM model, which predicts a target using a gradient boosting decision tree regression. 77public static LightGbmRegressionTrainer LightGbm(this RegressionCatalog.RegressionTrainers catalog,
LightGbmRegressionTrainer.cs (18)
15[assembly: LoadableClass(LightGbmRegressionTrainer.Summary, typeof(LightGbmRegressionTrainer), typeof(LightGbmRegressionTrainer.Options), 17LightGbmRegressionTrainer.UserNameValue, LightGbmRegressionTrainer.LoadNameValue, LightGbmRegressionTrainer.ShortName, DocName = "trainer/LightGBM.md")] 26/// Model parameters for <see cref="LightGbmRegressionTrainer"/>. 103/// <seealso cref="LightGbmExtensions.LightGbm(RegressionCatalog.RegressionTrainers, LightGbmRegressionTrainer.Options)"/> 105public sealed class LightGbmRegressionTrainer : LightGbmTrainerBase<LightGbmRegressionTrainer.Options, 118/// Options for the <see cref="LightGbmRegressionTrainer"/> as used in 160/// Initializes a new instance of <see cref="LightGbmRegressionTrainer"/> 197/// Initializes a new instance of <see cref="LightGbmRegressionTrainer"/> 258/// Trains a <see cref="LightGbmRegressionTrainer"/> using both training and validation data, returns 271Desc = LightGbmRegressionTrainer.Summary, 272UserName = LightGbmRegressionTrainer.UserNameValue, 273ShortName = LightGbmRegressionTrainer.ShortName)] 274public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, LightGbmRegressionTrainer.Options input) 281return TrainerEntryPointsUtils.Train<LightGbmRegressionTrainer.Options, CommonOutputs.RegressionOutput>(host, input,
Microsoft.ML.Samples (4)
Dynamic\Trainers\Regression\LightGbm.cs (1)
30var pipeline = mlContext.Regression.Trainers.
Dynamic\Trainers\Regression\LightGbmWithOptions.cs (2)
31var options = new LightGbmRegressionTrainer.Options 51var pipeline =
Dynamic\Trainers\Regression\LightGbmWithOptionsAdvanced.cs (1)
43new LightGbmRegressionTrainer.Options
Microsoft.ML.Tests (5)
FeatureContributionTests.cs (1)
59TestFeatureContribution(ML.Regression.Trainers.LightGbm(new LightGbmRegressionTrainer.Options() { UseCategoricalSplit = true }), GetOneHotEncodedData(numberOfInstances: 100), "LightGbmRegressionWithCategoricalSplit");
TrainerEstimators\TreeEstimators.cs (4)
234var trainer = ML.Regression.Trainers.LightGbm(new LightGbmRegressionTrainer.Options 966var trainer = ML.Regression.Trainers.LightGbm( 967new LightGbmRegressionTrainer.Options