3 instantiations of GamBinaryTrainer
Microsoft.ML.FastTree (3)
GamTrainer.cs (1)
708
() => new
GamBinaryTrainer
(host, input),
TreeTrainersCatalog.cs (2)
209
return new
GamBinaryTrainer
(env, labelColumnName, featureColumnName, exampleWeightColumnName, numberOfIterations, learningRate, maximumBinCountPerFeature);
229
return new
GamBinaryTrainer
(env, options);
34 references to GamBinaryTrainer
Microsoft.ML.FastTree (23)
GamClassification.cs (13)
16
[assembly: LoadableClass(
GamBinaryTrainer
.Summary,
17
typeof(
GamBinaryTrainer
), typeof(
GamBinaryTrainer
.Options),
19
GamBinaryTrainer
.UserNameValue,
20
GamBinaryTrainer
.LoadNameValue,
21
GamBinaryTrainer
.ShortName, DocName = "trainer/GAM.md")]
53
/// <seealso cref="TreeExtensions.Gam(BinaryClassificationCatalog.BinaryClassificationTrainers,
GamBinaryTrainer
.Options)"/>
56
GamTrainerBase<
GamBinaryTrainer
.Options,
61
/// Options for the <see cref="
GamBinaryTrainer
"/> as used in
83
/// Initializes a new instance of <see cref="
GamBinaryTrainer
"/>
92
/// Initializes a new instance of <see cref="
GamBinaryTrainer
"/>
177
/// Trains a <see cref="
GamBinaryTrainer
"/> using both training and validation data, returns
195
/// Model parameters for <see cref="
GamBinaryTrainer
"/>.
GamTrainer.cs (5)
699
[TlcModule.EntryPoint(Name = "Trainers.GeneralizedAdditiveModelBinaryClassifier", Desc =
GamBinaryTrainer
.Summary, UserName =
GamBinaryTrainer
.UserNameValue, ShortName =
GamBinaryTrainer
.ShortName)]
700
public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env,
GamBinaryTrainer
.Options input)
707
return TrainerEntryPointsUtils.Train<
GamBinaryTrainer
.Options, CommonOutputs.BinaryClassificationOutput>(host, input,
TreeTrainersCatalog.cs (5)
183
/// Create <see cref="
GamBinaryTrainer
"/>, which predicts a target using generalized additive models (GAM).
199
public static
GamBinaryTrainer
Gam(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
213
/// Create <see cref="
GamBinaryTrainer
"/> using advanced options, which predicts a target using generalized additive models (GAM).
224
public static
GamBinaryTrainer
Gam(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
225
GamBinaryTrainer
.Options options)
Microsoft.ML.IntegrationTests (2)
ModelFiles.cs (2)
139
var
pipeline = mlContext.BinaryClassification.Trainers.Gam();
272
var
pipeline = mlContext.BinaryClassification.Trainers.Gam();
Microsoft.ML.Samples (6)
Dynamic\Trainers\BinaryClassification\Gam.cs (1)
39
var
trainer = mlContext.BinaryClassification.Trainers
Dynamic\Trainers\BinaryClassification\GamWithOptions.cs (2)
42
var
trainer = mlContext.BinaryClassification.Trainers.Gam(
43
new
GamBinaryTrainer
.Options
Dynamic\Trainers\Regression\GamAdvanced.cs (1)
39
var
trainer = mlContext.BinaryClassification.Trainers.Gam(
Dynamic\Trainers\Regression\GamWithOptionsAdvanced.cs (2)
42
var
trainer = mlContext.BinaryClassification.Trainers.Gam(
43
new
GamBinaryTrainer
.Options
Microsoft.ML.Tests (3)
CalibratedModelParametersTests.cs (1)
50
new
GamBinaryTrainer
.Options { NumberOfThreads = 1 }).Fit(data);
TrainerEstimators\TreeEstimators.cs (2)
133
var
trainer = new GamBinaryTrainer(Env, new
GamBinaryTrainer
.Options