2 instantiations of SdcaLogisticRegressionBinaryTrainer
Microsoft.ML.StandardTrainers (2)
StandardTrainersCatalog.cs (2)
214return new SdcaLogisticRegressionBinaryTrainer(env, labelColumnName, featureColumnName, exampleWeightColumnName, l2Regularization, l1Regularization, maximumNumberOfIterations); 236return new SdcaLogisticRegressionBinaryTrainer(env, options);
35 references to SdcaLogisticRegressionBinaryTrainer
Microsoft.ML.AutoML (9)
API\AutoCatalog.cs (4)
325/// <param name="useSdcaLogisticRegression">true if use <see cref="SdcaLogisticRegressionBinaryTrainer"/> as available trainer.</param> 335/// <param name="sdcaLogisticRegressionSearchSpace">if provided, use it as search space for <see cref="SdcaLogisticRegressionBinaryTrainer"/>, otherwise the default search space will be used.</param> 418/// <param name="useSdcaLogisticRegression">true if use <see cref="SdcaLogisticRegressionBinaryTrainer"/> as available trainer.</param> 432/// <param name="sdcaLogisticRegressionSearchSpace">if provided, use it as search space for <see cref="SdcaLogisticRegressionBinaryTrainer"/>, otherwise the default search space will be used.</param>
API\BinaryClassificationExperiment.cs (1)
127/// See <see cref="SdcaLogisticRegressionBinaryTrainer"/>.
SweepableEstimator\Estimators\Sdca.cs (3)
49var option = new SdcaLogisticRegressionBinaryTrainer.Options() 67var option = new SdcaLogisticRegressionBinaryTrainer.Options() 77var binaryTrainer = context.BinaryClassification.Trainers.SdcaLogisticRegression(option);
TrainerExtensions\BinaryTrainerExtensions.cs (1)
160var options = TrainerExtensionUtil.CreateOptions<SdcaLogisticRegressionBinaryTrainer.Options>(sweepParams, columnInfo.LabelColumnName);
Microsoft.ML.IntegrationTests (3)
DataTransformation.cs (1)
146new SdcaLogisticRegressionBinaryTrainer.Options { NumberOfThreads = 1 }));
Training.cs (2)
45var sdcaTrainer = mlContext.BinaryClassification.Trainers.SdcaLogisticRegression( 46new SdcaLogisticRegressionBinaryTrainer.Options { NumberOfThreads = 1 });
Microsoft.ML.Samples (4)
Dynamic\Trainers\BinaryClassification\SdcaLogisticRegression.cs (1)
35var pipeline = mlContext.BinaryClassification.Trainers
Dynamic\Trainers\BinaryClassification\SdcaLogisticRegressionWithOptions.cs (2)
36var options = new SdcaLogisticRegressionBinaryTrainer.Options() 47var pipeline = mlContext.BinaryClassification.Trainers
Dynamic\Transforms\CalculateFeatureContributionCalibrated.cs (1)
36var linearTrainer = mlContext.BinaryClassification.Trainers
Microsoft.ML.SearchSpace.Tests (1)
SearchSpaceTest.cs (1)
285CreateAndVerifyDefaultSearchSpace<SdcaLogisticRegressionBinaryTrainer.Options>();
Microsoft.ML.StandardTrainers (10)
Standard\SdcaBinary.cs (5)
1440/// (2) <see cref="SdcaLogisticRegressionBinaryTrainer"/> essentially trains a regularized logistic regression model. Because logistic regression 1584/// <seealso cref="StandardTrainersCatalog.SdcaLogisticRegression(BinaryClassificationCatalog.BinaryClassificationTrainers, SdcaLogisticRegressionBinaryTrainer.Options)"/> 1590/// Options for the <see cref="SdcaLogisticRegressionBinaryTrainer"/> as used in 1740/// Comparing with <see cref="SdcaLogisticRegressionBinaryTrainer.CreatePredictor(VBuffer{float}[], float[])"/>, 2011/// Continues the training of a <see cref="SdcaLogisticRegressionBinaryTrainer"/> using an already trained <paramref name="modelParameters"/> and returns a <see cref="BinaryPredictionTransformer"/>.
StandardTrainersCatalog.cs (5)
188/// Create <see cref="SdcaLogisticRegressionBinaryTrainer"/>, which predicts a target using a linear classification model. 203public static SdcaLogisticRegressionBinaryTrainer SdcaLogisticRegression( 218/// Create <see cref="SdcaLogisticRegressionBinaryTrainer"/> with advanced options, which predicts a target using a linear classification model. 228public static SdcaLogisticRegressionBinaryTrainer SdcaLogisticRegression( 230SdcaLogisticRegressionBinaryTrainer.Options options)
Microsoft.ML.Tests (8)
TrainerEstimators\SdcaTests.cs (7)
31var binaryTrainer = ML.BinaryClassification.Trainers.SdcaLogisticRegression( 32new SdcaLogisticRegressionBinaryTrainer.Options { ConvergenceTolerance = 1e-2f, MaximumNumberOfIterations = 10 }); 75var pipeline = mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(labelColumnName: "Label", featureColumnName: "Features", l2Regularization: 0.001f); 118var sdcaWithoutWeightBinary = mlContext.BinaryClassification.Trainers.SdcaLogisticRegression( 119new SdcaLogisticRegressionBinaryTrainer.Options { NumberOfThreads = 1 }); 120var sdcaWithWeightBinary = mlContext.BinaryClassification.Trainers.SdcaLogisticRegression( 121new SdcaLogisticRegressionBinaryTrainer.Options { ExampleWeightColumnName = "Weight", NumberOfThreads = 1 });
TrainerEstimators\TreeEnsembleFeaturizerTest.cs (1)
338var naivePipeline = ML.BinaryClassification.Trainers.SdcaLogisticRegression("Label", "Features");