4 instantiations of FastForestBinaryTrainer
Microsoft.ML.FastTree (4)
RandomForestClassification.cs (1)
418() => new FastForestBinaryTrainer(host, input),
TreeEnsembleFeaturizationEstimator.cs (1)
330var trainer = new FastForestBinaryTrainer(Env, _trainerOptions);
TreeTrainersCatalog.cs (2)
415return new FastForestBinaryTrainer(env, labelColumnName, featureColumnName, exampleWeightColumnName, numberOfLeaves, numberOfTrees, minimumExampleCountPerLeaf); 437return new FastForestBinaryTrainer(env, options);
53 references to FastForestBinaryTrainer
Microsoft.ML.AutoML (7)
API\BinaryClassificationExperiment.cs (1)
107/// See <see cref="FastForestBinaryTrainer"/>.
API\MulticlassClassificationExperiment.cs (1)
86/// <see cref="OneVersusAllTrainer"/> using <see cref="FastForestBinaryTrainer"/>.
SweepableEstimator\Estimators\FastForest.cs (2)
13var option = new FastForestBinaryTrainer.Options() 49var option = new FastForestBinaryTrainer.Options()
TrainerExtensions\BinaryTrainerExtensions.cs (1)
74var options = TrainerExtensionUtil.CreateOptions<FastForestBinaryTrainer.Options>(sweepParams, columnInfo.LabelColumnName);
TrainerExtensions\MultiTrainerExtensions.cs (2)
50var binaryTrainer = _binaryLearnerCatalogItem.CreateInstance(mlContext, sweepParams, columnInfo) as FastForestBinaryTrainer;
Microsoft.ML.Core.Tests (1)
UnitTests\TestEntryPoints.cs (1)
1529var fastForest = new FastForestBinaryTrainer(Env, "Label", "Features");
Microsoft.ML.FastTree (28)
RandomForestClassification.cs (18)
20[assembly: LoadableClass(FastForestBinaryTrainer.Summary, typeof(FastForestBinaryTrainer), typeof(FastForestBinaryTrainer.Options), 22FastForestBinaryTrainer.UserNameValue, 23FastForestBinaryTrainer.LoadNameValue, 25FastForestBinaryTrainer.ShortName, 56/// Model parameters for <see cref="FastForestBinaryTrainer"/>. 144/// <seealso cref="TreeExtensions.FastForest(BinaryClassificationCatalog.BinaryClassificationTrainers, FastForestBinaryTrainer.Options)"/> 147RandomForestTrainerBase<FastForestBinaryTrainer.Options, BinaryPredictionTransformer<FastForestBinaryModelParameters>, FastForestBinaryModelParameters> 150/// Options for the <see cref="FastForestBinaryTrainer"/> as used in 179/// Initializes a new instance of <see cref="FastForestBinaryTrainer"/> 202/// Initializes a new instance of <see cref="FastForestBinaryTrainer"/> by using the <see cref="Options"/> class. 369/// Trains a <see cref="FastForestBinaryTrainer"/> using both training and validation data, returns 407Desc = FastForestBinaryTrainer.Summary, 408UserName = FastForestBinaryTrainer.UserNameValue, 409ShortName = FastForestBinaryTrainer.ShortName)] 410public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, FastForestBinaryTrainer.Options input) 417return TrainerEntryPointsUtils.Train<FastForestBinaryTrainer.Options, CommonOutputs.BinaryClassificationOutput>(host, input,
TreeEnsembleFeaturizationEstimator.cs (4)
309private readonly FastForestBinaryTrainer.Options _trainerOptions; 317/// The configuration of <see cref="FastForestBinaryTrainer"/> used to train the underlying <see cref="TreeEnsembleModelParameters"/>. 319public FastForestBinaryTrainer.Options TrainerOptions; 330var trainer = new FastForestBinaryTrainer(Env, _trainerOptions);
TreeTrainersCatalog.cs (6)
389/// Create <see cref="FastForestBinaryTrainer"/>, which predicts a target using a decision tree regression model. 405public static FastForestBinaryTrainer FastForest(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog, 419/// Create <see cref="FastForestBinaryTrainer"/> with advanced options, which predicts a target using a decision tree regression model. 430public static FastForestBinaryTrainer FastForest(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog, 431FastForestBinaryTrainer.Options options) 504/// Create <see cref="FastForestBinaryFeaturizationEstimator"/>, which uses <see cref="FastForestBinaryTrainer"/> to train <see cref="TreeEnsembleModelParameters"/> to create tree-based features.
Microsoft.ML.Samples (4)
Dynamic\Trainers\BinaryClassification\FastForest.cs (1)
30var pipeline = mlContext.BinaryClassification.Trainers
Dynamic\Trainers\BinaryClassification\FastForestWithOptions.cs (2)
31var options = new FastForestBinaryTrainer.Options 42var pipeline = mlContext.BinaryClassification.Trainers
Dynamic\Transforms\TreeFeaturization\FastForestBinaryFeaturizationWithOptions.cs (1)
46var trainerOptions = new FastForestBinaryTrainer.Options
Microsoft.ML.Samples.OneDal (3)
Program.cs (3)
71FastForestBinaryTrainer.Options options = new FastForestBinaryTrainer.Options(); 79var trainer = mlContext.BinaryClassification.Trainers.FastForest(options);
Microsoft.ML.Tests (10)
PermutationFeatureImportanceTests.cs (1)
488var ff = ML.BinaryClassification.Trainers.FastForest();
TrainerEstimators\OneDalEstimators.cs (3)
64FastForestBinaryTrainer.Options options = new FastForestBinaryTrainer.Options(); 72var trainer = ML.BinaryClassification.Trainers.FastForest(options);
TrainerEstimators\TreeEnsembleFeaturizerTest.cs (3)
184var trainer = ML.BinaryClassification.Trainers.FastForest( 185new FastForestBinaryTrainer.Options 396var trainerOptions = new FastForestBinaryTrainer.Options
TrainerEstimators\TreeEstimators.cs (3)
152var trainer = ML.BinaryClassification.Trainers.FastForest( 153new FastForestBinaryTrainer.Options 1007new FastForestBinaryTrainer.Options { NumberOfTrees = 2, NumberOfThreads = 1, NumberOfLeaves = 4, CategoricalSplit = true }));