diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/SDCA.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/SDCA.cs
index 5f08dee906..ca4f9531a7 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/SDCA.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/SDCA.cs
@@ -1,6 +1,7 @@
 using System;
 using System.Linq;
 using Microsoft.ML.Data;
+using Microsoft.ML.Trainers;
 
 namespace Microsoft.ML.Samples.Dynamic
 {
@@ -59,15 +60,13 @@ public static void SDCA_BinaryClassification()
             // If we wanted to specify more advanced parameters for the algorithm, 
             // we could do so by tweaking the 'advancedSetting'.
             var advancedPipeline = mlContext.Transforms.Text.FeaturizeText("SentimentText", "Features")
-                                  .Append(mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscent
-                                  (labelColumn: "Sentiment",
-                                   featureColumn: "Features",
-                                   advancedSettings: s=>
-                                       {
-                                           s.ConvergenceTolerance = 0.01f;   // The learning rate for adjusting bias from being regularized
-                                           s.NumThreads = 2;            // Degree of lock-free parallelism 
-                                       })
-                                   );
+                                  .Append(mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+                                      new SdcaBinaryTrainer.Options { 
+                                        LabelColumn = "Sentiment",
+                                        FeatureColumn = "Features",
+                                        ConvergenceTolerance = 0.01f,  // The learning rate for adjusting bias from being regularized
+                                        NumThreads = 2, // Degree of lock-free parallelism 
+                                      }));
 
             // Run Cross-Validation on this second pipeline.
             var cvResults_advancedPipeline = mlContext.BinaryClassification.CrossValidate(data, pipeline, labelColumn: "Sentiment", numFolds: 3);
diff --git a/src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs b/src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs
index b4c5f1a506..313f66837d 100644
--- a/src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs
+++ b/src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs
@@ -47,6 +47,8 @@ public static FastTreeRegressionTrainer FastTree(this RegressionContext.Regressi
             FastTreeRegressionTrainer.Options options)
         {
             Contracts.CheckValue(ctx, nameof(ctx));
+            Contracts.CheckValue(options, nameof(options));
+
             var env = CatalogUtils.GetEnvironment(ctx);
             return new FastTreeRegressionTrainer(env, options);
         }
@@ -85,6 +87,8 @@ public static FastTreeBinaryClassificationTrainer FastTree(this BinaryClassifica
             FastTreeBinaryClassificationTrainer.Options options)
         {
             Contracts.CheckValue(ctx, nameof(ctx));
+            Contracts.CheckValue(options, nameof(options));
+
             var env = CatalogUtils.GetEnvironment(ctx);
             return new FastTreeBinaryClassificationTrainer(env, options);
         }
@@ -125,6 +129,8 @@ public static FastTreeRankingTrainer FastTree(this RankingContext.RankingTrainer
             FastTreeRankingTrainer.Options options)
         {
             Contracts.CheckValue(ctx, nameof(ctx));
+            Contracts.CheckValue(options, nameof(options));
+
             var env = CatalogUtils.GetEnvironment(ctx);
             return new FastTreeRankingTrainer(env, options);
         }
@@ -213,6 +219,8 @@ public static FastTreeTweedieTrainer FastTreeTweedie(this RegressionContext.Regr
             FastTreeTweedieTrainer.Options options)
         {
             Contracts.CheckValue(ctx, nameof(ctx));
+            Contracts.CheckValue(options, nameof(options));
+
             var env = CatalogUtils.GetEnvironment(ctx);
             return new FastTreeTweedieTrainer(env, options);
         }
@@ -251,6 +259,8 @@ public static FastForestRegression FastForest(this RegressionContext.RegressionT
             FastForestRegression.Options options)
         {
             Contracts.CheckValue(ctx, nameof(ctx));
+            Contracts.CheckValue(options, nameof(options));
+
             var env = CatalogUtils.GetEnvironment(ctx);
             return new FastForestRegression(env, options);
         }
@@ -289,6 +299,8 @@ public static FastForestClassification FastForest(this BinaryClassificationConte
             FastForestClassification.Options options)
         {
             Contracts.CheckValue(ctx, nameof(ctx));
+            Contracts.CheckValue(options, nameof(options));
+
             var env = CatalogUtils.GetEnvironment(ctx);
             return new FastForestClassification(env, options);
         }
diff --git a/src/Microsoft.ML.StandardLearners/Standard/SdcaBinary.cs b/src/Microsoft.ML.StandardLearners/Standard/SdcaBinary.cs
index 1648314c9d..01c57d27f9 100644
--- a/src/Microsoft.ML.StandardLearners/Standard/SdcaBinary.cs
+++ b/src/Microsoft.ML.StandardLearners/Standard/SdcaBinary.cs
@@ -23,7 +23,7 @@
 using Microsoft.ML.Training;
 using Microsoft.ML.Transforms;
 
-[assembly: LoadableClass(typeof(SdcaBinaryTrainer), typeof(SdcaBinaryTrainer.Arguments),
+[assembly: LoadableClass(typeof(SdcaBinaryTrainer), typeof(SdcaBinaryTrainer.Options),
     new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer), typeof(SignatureFeatureScorerTrainer) },
     SdcaBinaryTrainer.UserNameValue,
     SdcaBinaryTrainer.LoadNameValue,
@@ -31,7 +31,7 @@
     "lc",
     "sasdca")]
 
-[assembly: LoadableClass(typeof(StochasticGradientDescentClassificationTrainer), typeof(StochasticGradientDescentClassificationTrainer.Arguments),
+[assembly: LoadableClass(typeof(StochasticGradientDescentClassificationTrainer), typeof(StochasticGradientDescentClassificationTrainer.Options),
     new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer), typeof(SignatureFeatureScorerTrainer) },
     StochasticGradientDescentClassificationTrainer.UserNameValue,
     StochasticGradientDescentClassificationTrainer.LoadNameValue,
@@ -246,21 +246,19 @@ protected enum MetricKind
 
         private const string RegisterName = nameof(SdcaTrainerBase<TArgs, TTransformer, TModel>);
 
-        private static TArgs ArgsInit(string featureColumn, SchemaShape.Column labelColumn, Action<TArgs> advancedSettings = null)
+        private static TArgs ArgsInit(string featureColumn, SchemaShape.Column labelColumn)
         {
             var args = new TArgs();
 
-            // Apply the advanced args, if the user supplied any.
-            advancedSettings?.Invoke(args);
             args.FeatureColumn = featureColumn;
             args.LabelColumn = labelColumn.Name;
             return args;
         }
 
         internal SdcaTrainerBase(IHostEnvironment env, string featureColumn, SchemaShape.Column labelColumn,
-           SchemaShape.Column weight = default, Action<TArgs> advancedSettings = null, float? l2Const = null,
+           SchemaShape.Column weight = default, float? l2Const = null,
             float? l1Threshold = null, int? maxIterations = null)
-          : this(env, ArgsInit(featureColumn, labelColumn, advancedSettings), labelColumn, weight, l2Const, l1Threshold, maxIterations)
+          : this(env, ArgsInit(featureColumn, labelColumn), labelColumn, weight, l2Const, l1Threshold, maxIterations)
         {
         }
 
@@ -1391,12 +1389,12 @@ public void Add(Double summand)
         }
     }
 
-    public sealed class SdcaBinaryTrainer : SdcaTrainerBase<SdcaBinaryTrainer.Arguments, BinaryPredictionTransformer<TScalarPredictor>, TScalarPredictor>
+    public sealed class SdcaBinaryTrainer : SdcaTrainerBase<SdcaBinaryTrainer.Options, BinaryPredictionTransformer<TScalarPredictor>, TScalarPredictor>
     {
-        public const string LoadNameValue = "SDCA";
+        internal const string LoadNameValue = "SDCA";
         internal const string UserNameValue = "Fast Linear (SA-SDCA)";
 
-        public sealed class Arguments : ArgumentsBase
+        public sealed class Options : ArgumentsBase
         {
             [Argument(ArgumentType.Multiple, HelpText = "Loss Function", ShortName = "loss", SortOrder = 50)]
             public ISupportSdcaClassificationLossFactory LossFunction = new LogLossFactory();
@@ -1441,21 +1439,16 @@ internal override void Check(IHostEnvironment env)
         /// <param name="l2Const">The L2 regularization hyperparameter.</param>
         /// <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
         /// <param name="maxIterations">The maximum number of passes to perform over the data.</param>
-        /// <param name="advancedSettings">A delegate to set more settings.
-        /// The settings here will override the ones provided in the direct method signature,
-        /// if both are present and have different values.
-        /// The columns names, however need to be provided directly, not through the <paramref name="advancedSettings"/>.</param>
-        public SdcaBinaryTrainer(IHostEnvironment env,
+        internal SdcaBinaryTrainer(IHostEnvironment env,
             string labelColumn = DefaultColumnNames.Label,
             string featureColumn = DefaultColumnNames.Features,
             string weightColumn = null,
             ISupportSdcaClassificationLoss loss = null,
             float? l2Const = null,
             float? l1Threshold = null,
-            int? maxIterations = null,
-            Action<Arguments> advancedSettings = null)
-             : base(env, featureColumn, TrainerUtils.MakeBoolScalarLabel(labelColumn), TrainerUtils.MakeR4ScalarWeightColumn(weightColumn), advancedSettings,
-                  l2Const, l1Threshold, maxIterations)
+            int? maxIterations = null)
+             : base(env, featureColumn, TrainerUtils.MakeBoolScalarLabel(labelColumn), TrainerUtils.MakeR4ScalarWeightColumn(weightColumn),
+                   l2Const, l1Threshold, maxIterations)
         {
             Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
             Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
@@ -1495,11 +1488,10 @@ public SdcaBinaryTrainer(IHostEnvironment env,
             _outputColumns = outCols.ToArray();
         }
 
-        internal SdcaBinaryTrainer(IHostEnvironment env, Arguments args,
-            string featureColumn, string labelColumn, string weightColumn = null)
-            : base(env, args, TrainerUtils.MakeBoolScalarLabel(labelColumn), TrainerUtils.MakeR4ScalarWeightColumn(weightColumn))
+        internal SdcaBinaryTrainer(IHostEnvironment env, Options options)
+            : base(env, options, TrainerUtils.MakeBoolScalarLabel(options.LabelColumn))
         {
-            _loss = args.LossFunction.CreateComponent(env);
+            _loss = options.LossFunction.CreateComponent(env);
             Loss = _loss;
             Info = new TrainerInfo(calibration: !(_loss is LogLoss));
             _positiveInstanceWeight = Args.PositiveInstanceWeight;
@@ -1533,12 +1525,6 @@ internal SdcaBinaryTrainer(IHostEnvironment env, Arguments args,
             };
 
             _outputColumns = outCols.ToArray();
-
-        }
-
-        public SdcaBinaryTrainer(IHostEnvironment env, Arguments args)
-            : this(env, args, args.FeatureColumn, args.LabelColumn)
-        {
         }
 
         protected override void CheckLabelCompatible(SchemaShape.Column labelCol)
@@ -1594,7 +1580,7 @@ public sealed class StochasticGradientDescentClassificationTrainer :
         internal const string UserNameValue = "Hogwild SGD (binary)";
         internal const string ShortName = "HogwildSGD";
 
-        public sealed class Arguments : LearnerInputBaseWithWeight
+        public sealed class Options : LearnerInputBaseWithWeight
         {
             [Argument(ArgumentType.Multiple, HelpText = "Loss Function", ShortName = "loss", SortOrder = 50)]
             public ISupportClassificationLossFactory LossFunction = new LogLossFactory();
@@ -1669,9 +1655,9 @@ internal static class Defaults
         }
 
         private readonly IClassificationLoss _loss;
-        private readonly Arguments _args;
+        private readonly Options _options;
 
-        protected override bool ShuffleData => _args.Shuffle;
+        protected override bool ShuffleData => _options.Shuffle;
 
         public override PredictionKind PredictionKind => PredictionKind.BinaryClassification;
 
@@ -1688,53 +1674,50 @@ internal static class Defaults
         /// <param name="initLearningRate">The initial learning rate used by SGD.</param>
         /// <param name="l2Weight">The L2 regularizer constant.</param>
         /// <param name="loss">The loss function to use.</param>
-        /// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
-        public StochasticGradientDescentClassificationTrainer(IHostEnvironment env,
+        internal StochasticGradientDescentClassificationTrainer(IHostEnvironment env,
             string labelColumn = DefaultColumnNames.Label,
             string featureColumn = DefaultColumnNames.Features,
             string weightColumn = null,
-            int maxIterations = Arguments.Defaults.MaxIterations,
-            double initLearningRate = Arguments.Defaults.InitLearningRate,
-            float l2Weight = Arguments.Defaults.L2Weight,
-            ISupportClassificationLossFactory loss = null,
-            Action<Arguments> advancedSettings = null)
+            int maxIterations = Options.Defaults.MaxIterations,
+            double initLearningRate = Options.Defaults.InitLearningRate,
+            float l2Weight = Options.Defaults.L2Weight,
+            ISupportClassificationLossFactory loss = null)
             : base(env, featureColumn, TrainerUtils.MakeBoolScalarLabel(labelColumn), weightColumn)
         {
             Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
             Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
 
-            _args = new Arguments();
-            _args.MaxIterations = maxIterations;
-            _args.InitLearningRate = initLearningRate;
-            _args.L2Weight = l2Weight;
-
-            // Apply the advanced args, if the user supplied any.
-            advancedSettings?.Invoke(_args);
+            _options = new Options();
+            _options.MaxIterations = maxIterations;
+            _options.InitLearningRate = initLearningRate;
+            _options.L2Weight = l2Weight;
 
-            _args.FeatureColumn = featureColumn;
-            _args.LabelColumn = labelColumn;
-            _args.WeightColumn = weightColumn;
+            _options.FeatureColumn = featureColumn;
+            _options.LabelColumn = labelColumn;
+            _options.WeightColumn = weightColumn != null ? Optional<string>.Explicit(weightColumn) : Optional<string>.Implicit(DefaultColumnNames.Weight);
 
             if (loss != null)
-                _args.LossFunction = loss;
-            _args.Check(env);
+                _options.LossFunction = loss;
+            _options.Check(env);
 
-            _loss = _args.LossFunction.CreateComponent(env);
+            _loss = _options.LossFunction.CreateComponent(env);
             Info = new TrainerInfo(calibration: !(_loss is LogLoss), supportIncrementalTrain: true);
-            NeedShuffle = _args.Shuffle;
+            NeedShuffle = _options.Shuffle;
         }
 
         /// <summary>
         /// Initializes a new instance of <see cref="StochasticGradientDescentClassificationTrainer"/>
         /// </summary>
-        internal StochasticGradientDescentClassificationTrainer(IHostEnvironment env, Arguments args)
-            : base(env, args.FeatureColumn, TrainerUtils.MakeBoolScalarLabel(args.LabelColumn), args.WeightColumn)
+        /// <param name="env">The environment to use.</param>
+        /// <param name="options">Advanced arguments to the algorithm.</param>
+        internal StochasticGradientDescentClassificationTrainer(IHostEnvironment env, Options options)
+            : base(env, options.FeatureColumn, TrainerUtils.MakeBoolScalarLabel(options.LabelColumn), options.WeightColumn.IsExplicit ? options.WeightColumn.Value : null)
         {
-            args.Check(env);
-            _loss = args.LossFunction.CreateComponent(env);
+            options.Check(env);
+            _loss = options.LossFunction.CreateComponent(env);
             Info = new TrainerInfo(calibration: !(_loss is LogLoss), supportIncrementalTrain: true);
-            NeedShuffle = args.Shuffle;
-            _args = args;
+            NeedShuffle = options.Shuffle;
+            _options = options;
         }
 
         protected override SchemaShape.Column[] GetOutputColumnsCore(SchemaShape inputSchema)
@@ -1767,22 +1750,22 @@ private protected override TScalarPredictor TrainCore(IChannel ch, RoleMappedDat
             var cursorFactory = new FloatLabelCursor.Factory(data, CursOpt.Label | CursOpt.Features | CursOpt.Weight);
 
             int numThreads;
-            if (_args.NumThreads.HasValue)
+            if (_options.NumThreads.HasValue)
             {
-                numThreads = _args.NumThreads.Value;
-                ch.CheckUserArg(numThreads > 0, nameof(_args.NumThreads), "The number of threads must be either null or a positive integer.");
+                numThreads = _options.NumThreads.Value;
+                ch.CheckUserArg(numThreads > 0, nameof(_options.NumThreads), "The number of threads must be either null or a positive integer.");
             }
             else
                 numThreads = ComputeNumThreads(cursorFactory);
 
             ch.Assert(numThreads > 0);
-            int checkFrequency = _args.CheckFrequency ?? numThreads;
+            int checkFrequency = _options.CheckFrequency ?? numThreads;
             if (checkFrequency <= 0)
                 checkFrequency = int.MaxValue;
-            var l2Weight = _args.L2Weight;
+            var l2Weight = _options.L2Weight;
             var lossFunc = _loss;
             var pOptions = new ParallelOptions { MaxDegreeOfParallelism = numThreads };
-            var positiveInstanceWeight = _args.PositiveInstanceWeight;
+            var positiveInstanceWeight = _options.PositiveInstanceWeight;
             var weights = default(VBuffer<float>);
             float bias = 0.0f;
             if (predictor != null)
@@ -1804,7 +1787,7 @@ private protected override TScalarPredictor TrainCore(IChannel ch, RoleMappedDat
             // REVIEW: Investigate using parallel row cursor set instead of getting cursor independently. The convergence of SDCA need to be verified.
             Action<int, IProgressChannel> checkConvergence = (e, pch) =>
             {
-                if (e % checkFrequency == 0 && e != _args.MaxIterations)
+                if (e % checkFrequency == 0 && e != _options.MaxIterations)
                 {
                     Double trainTime = watch.Elapsed.TotalSeconds;
                     var lossSum = new CompensatedSum();
@@ -1830,8 +1813,8 @@ private protected override TScalarPredictor TrainCore(IChannel ch, RoleMappedDat
                     improvement = improvement == 0 ? loss - newLoss : 0.5 * (loss - newLoss + improvement);
                     loss = newLoss;
 
-                    pch.Checkpoint(loss, improvement, e, _args.MaxIterations);
-                    converged = improvement < _args.ConvergenceTolerance;
+                    pch.Checkpoint(loss, improvement, e, _options.MaxIterations);
+                    converged = improvement < _options.ConvergenceTolerance;
                 }
             };
 
@@ -1840,17 +1823,17 @@ private protected override TScalarPredictor TrainCore(IChannel ch, RoleMappedDat
             //Reference: Leon Bottou. Stochastic Gradient Descent Tricks.
             //https://research.microsoft.com/pubs/192769/tricks-2012.pdf
 
-            var trainingTasks = new Action<Random, IProgressChannel>[_args.MaxIterations];
-            var rands = new Random[_args.MaxIterations];
-            var ilr = _args.InitLearningRate;
+            var trainingTasks = new Action<Random, IProgressChannel>[_options.MaxIterations];
+            var rands = new Random[_options.MaxIterations];
+            var ilr = _options.InitLearningRate;
             long t = 0;
-            for (int epoch = 1; epoch <= _args.MaxIterations; epoch++)
+            for (int epoch = 1; epoch <= _options.MaxIterations; epoch++)
             {
                 int e = epoch; //localize the modified closure
                 rands[e - 1] = RandomUtils.Create(Host.Rand.Next());
                 trainingTasks[e - 1] = (rand, pch) =>
                 {
-                    using (var cursor = _args.Shuffle ? cursorFactory.Create(rand) : cursorFactory.Create())
+                    using (var cursor = _options.Shuffle ? cursorFactory.Create(rand) : cursorFactory.Create())
                     {
                         var weightsEditor = VBufferEditor.CreateFromBuffer(ref weights);
                         while (cursor.MoveNext())
@@ -1905,9 +1888,9 @@ private protected override TScalarPredictor TrainCore(IChannel ch, RoleMappedDat
                 {
                     int iter = 0;
                     pch.SetHeader(new ProgressHeader(new[] { "Loss", "Improvement" }, new[] { "iterations" }),
-                        entry => entry.SetProgress(0, iter, _args.MaxIterations));
+                        entry => entry.SetProgress(0, iter, _options.MaxIterations));
                     // Synchorized SGD.
-                    for (int i = 0; i < _args.MaxIterations; i++)
+                    for (int i = 0; i < _options.MaxIterations; i++)
                     {
                         iter = i;
                         trainingTasks[i](rands[i], pch);
@@ -1925,7 +1908,7 @@ private protected override TScalarPredictor TrainCore(IChannel ch, RoleMappedDat
                             // REVIEW: technically, we could keep track of how many iterations have started,
                             // but this needs more synchronization than Parallel.For allows.
                         });
-                    Parallel.For(0, _args.MaxIterations, pOptions, i => trainingTasks[i](rands[i], pch));
+                    Parallel.For(0, _options.MaxIterations, pOptions, i => trainingTasks[i](rands[i], pch));
                     //note that P.Invoke will wait until all tasks finish
                 }
             }
@@ -1947,14 +1930,14 @@ private protected override void CheckLabel(RoleMappedData examples, out int weig
         }
 
         [TlcModule.EntryPoint(Name = "Trainers.StochasticGradientDescentBinaryClassifier", Desc = "Train an Hogwild SGD binary model.", UserName = UserNameValue, ShortName = ShortName)]
-        public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, Arguments input)
+        public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, Options input)
         {
             Contracts.CheckValue(env, nameof(env));
             var host = env.Register("TrainHogwildSGD");
             host.CheckValue(input, nameof(input));
             EntryPointUtils.CheckInputArgs(host, input);
 
-            return LearnerEntryPointsUtils.Train<Arguments, CommonOutputs.BinaryClassificationOutput>(host, input,
+            return LearnerEntryPointsUtils.Train<Options, CommonOutputs.BinaryClassificationOutput>(host, input,
                 () => new StochasticGradientDescentClassificationTrainer(host, input),
                 () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn),
                 () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.WeightColumn),
@@ -1974,14 +1957,14 @@ public static partial class Sdca
             ShortName = SdcaBinaryTrainer.LoadNameValue,
             XmlInclude = new[] { @"<include file='../Microsoft.ML.StandardLearners/Standard/doc.xml' path='doc/members/member[@name=""SDCA""]/*' />",
                                  @"<include file='../Microsoft.ML.StandardLearners/Standard/doc.xml' path='doc/members/example[@name=""StochasticDualCoordinateAscentBinaryClassifier""]/*'/>" })]
-        public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, SdcaBinaryTrainer.Arguments input)
+        public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, SdcaBinaryTrainer.Options input)
         {
             Contracts.CheckValue(env, nameof(env));
             var host = env.Register("TrainSDCA");
             host.CheckValue(input, nameof(input));
             EntryPointUtils.CheckInputArgs(host, input);
 
-            return LearnerEntryPointsUtils.Train<SdcaBinaryTrainer.Arguments, CommonOutputs.BinaryClassificationOutput>(host, input,
+            return LearnerEntryPointsUtils.Train<SdcaBinaryTrainer.Options, CommonOutputs.BinaryClassificationOutput>(host, input,
                 () => new SdcaBinaryTrainer(host, input),
                 () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn),
                 calibrator: input.Calibrator, maxCalibrationExamples: input.MaxCalibrationExamples);
diff --git a/src/Microsoft.ML.StandardLearners/Standard/SdcaMultiClass.cs b/src/Microsoft.ML.StandardLearners/Standard/SdcaMultiClass.cs
index 0a069f5d8c..873ee81d41 100644
--- a/src/Microsoft.ML.StandardLearners/Standard/SdcaMultiClass.cs
+++ b/src/Microsoft.ML.StandardLearners/Standard/SdcaMultiClass.cs
@@ -19,7 +19,7 @@
 using Microsoft.ML.Training;
 using Float = System.Single;
 
-[assembly: LoadableClass(SdcaMultiClassTrainer.Summary, typeof(SdcaMultiClassTrainer), typeof(SdcaMultiClassTrainer.Arguments),
+[assembly: LoadableClass(SdcaMultiClassTrainer.Summary, typeof(SdcaMultiClassTrainer), typeof(SdcaMultiClassTrainer.Options),
     new[] { typeof(SignatureMultiClassClassifierTrainer), typeof(SignatureTrainer), typeof(SignatureFeatureScorerTrainer) },
     SdcaMultiClassTrainer.UserNameValue,
     SdcaMultiClassTrainer.LoadNameValue,
@@ -29,14 +29,14 @@ namespace Microsoft.ML.Trainers
 {
     // SDCA linear multiclass trainer.
     /// <include file='doc.xml' path='doc/members/member[@name="SDCA"]/*' />
-    public class SdcaMultiClassTrainer : SdcaTrainerBase<SdcaMultiClassTrainer.Arguments, MulticlassPredictionTransformer<MulticlassLogisticRegressionModelParameters>, MulticlassLogisticRegressionModelParameters>
+    public class SdcaMultiClassTrainer : SdcaTrainerBase<SdcaMultiClassTrainer.Options, MulticlassPredictionTransformer<MulticlassLogisticRegressionModelParameters>, MulticlassLogisticRegressionModelParameters>
     {
         public const string LoadNameValue = "SDCAMC";
         public const string UserNameValue = "Fast Linear Multi-class Classification (SA-SDCA)";
         public const string ShortName = "sasdcamc";
         internal const string Summary = "The SDCA linear multi-class classification trainer.";
 
-        public sealed class Arguments : ArgumentsBase
+        public sealed class Options : ArgumentsBase
         {
             [Argument(ArgumentType.Multiple, HelpText = "Loss Function", ShortName = "loss", SortOrder = 50)]
             public ISupportSdcaClassificationLossFactory LossFunction = new LogLossFactory();
@@ -57,21 +57,16 @@ public sealed class Arguments : ArgumentsBase
         /// <param name="l2Const">The L2 regularization hyperparameter.</param>
         /// <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
         /// <param name="maxIterations">The maximum number of passes to perform over the data.</param>
-        /// <param name="advancedSettings">A delegate to set more settings.
-        /// The settings here will override the ones provided in the direct method signature,
-        /// if both are present and have different values.
-        /// The columns names, however need to be provided directly, not through the <paramref name="advancedSettings"/>.</param>
-        public SdcaMultiClassTrainer(IHostEnvironment env,
+        internal SdcaMultiClassTrainer(IHostEnvironment env,
             string labelColumn = DefaultColumnNames.Label,
             string featureColumn = DefaultColumnNames.Features,
             string weights = null,
             ISupportSdcaClassificationLoss loss = null,
             float? l2Const = null,
             float? l1Threshold = null,
-            int? maxIterations = null,
-            Action<Arguments> advancedSettings = null)
-             : base(env, featureColumn, TrainerUtils.MakeU4ScalarColumn(labelColumn), TrainerUtils.MakeR4ScalarWeightColumn(weights), advancedSettings,
-                  l2Const, l1Threshold, maxIterations)
+            int? maxIterations = null)
+             : base(env, featureColumn, TrainerUtils.MakeU4ScalarColumn(labelColumn), TrainerUtils.MakeR4ScalarWeightColumn(weights),
+                   l2Const, l1Threshold, maxIterations)
         {
             Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
             Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
@@ -79,19 +74,19 @@ public SdcaMultiClassTrainer(IHostEnvironment env,
             Loss = _loss;
         }
 
-        internal SdcaMultiClassTrainer(IHostEnvironment env, Arguments args,
+        internal SdcaMultiClassTrainer(IHostEnvironment env, Options options,
             string featureColumn, string labelColumn, string weightColumn = null)
-            : base(env, args, TrainerUtils.MakeU4ScalarColumn(labelColumn), TrainerUtils.MakeR4ScalarWeightColumn(weightColumn))
+            : base(env, options, TrainerUtils.MakeU4ScalarColumn(labelColumn), TrainerUtils.MakeR4ScalarWeightColumn(weightColumn))
         {
             Host.CheckValue(labelColumn, nameof(labelColumn));
             Host.CheckValue(featureColumn, nameof(featureColumn));
 
-            _loss = args.LossFunction.CreateComponent(env);
+            _loss = options.LossFunction.CreateComponent(env);
             Loss = _loss;
         }
 
-        internal SdcaMultiClassTrainer(IHostEnvironment env, Arguments args)
-            : this(env, args, args.FeatureColumn, args.LabelColumn)
+        internal SdcaMultiClassTrainer(IHostEnvironment env, Options options)
+            : this(env, options, options.FeatureColumn, options.LabelColumn)
         {
         }
 
@@ -455,14 +450,14 @@ public static partial class Sdca
             ShortName = SdcaMultiClassTrainer.ShortName,
             XmlInclude = new[] { @"<include file='../Microsoft.ML.StandardLearners/Standard/doc.xml' path='doc/members/member[@name=""SDCA""]/*' />",
                                  @"<include file='../Microsoft.ML.StandardLearners/Standard/doc.xml' path='doc/members/example[@name=""StochasticDualCoordinateAscentClassifier""]/*' />" })]
-        public static CommonOutputs.MulticlassClassificationOutput TrainMultiClass(IHostEnvironment env, SdcaMultiClassTrainer.Arguments input)
+        public static CommonOutputs.MulticlassClassificationOutput TrainMultiClass(IHostEnvironment env, SdcaMultiClassTrainer.Options input)
         {
             Contracts.CheckValue(env, nameof(env));
             var host = env.Register("TrainSDCA");
             host.CheckValue(input, nameof(input));
             EntryPointUtils.CheckInputArgs(host, input);
 
-            return LearnerEntryPointsUtils.Train<SdcaMultiClassTrainer.Arguments, CommonOutputs.MulticlassClassificationOutput>(host, input,
+            return LearnerEntryPointsUtils.Train<SdcaMultiClassTrainer.Options, CommonOutputs.MulticlassClassificationOutput>(host, input,
                 () => new SdcaMultiClassTrainer(host, input),
                 () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn));
         }
diff --git a/src/Microsoft.ML.StandardLearners/Standard/SdcaRegression.cs b/src/Microsoft.ML.StandardLearners/Standard/SdcaRegression.cs
index 8700f69605..b8a8b286e1 100644
--- a/src/Microsoft.ML.StandardLearners/Standard/SdcaRegression.cs
+++ b/src/Microsoft.ML.StandardLearners/Standard/SdcaRegression.cs
@@ -15,7 +15,7 @@
 using Microsoft.ML.Trainers;
 using Microsoft.ML.Training;
 
-[assembly: LoadableClass(SdcaRegressionTrainer.Summary, typeof(SdcaRegressionTrainer), typeof(SdcaRegressionTrainer.Arguments),
+[assembly: LoadableClass(SdcaRegressionTrainer.Summary, typeof(SdcaRegressionTrainer), typeof(SdcaRegressionTrainer.Options),
     new[] { typeof(SignatureRegressorTrainer), typeof(SignatureTrainer), typeof(SignatureFeatureScorerTrainer) },
     SdcaRegressionTrainer.UserNameValue,
     SdcaRegressionTrainer.LoadNameValue,
@@ -24,19 +24,19 @@
 namespace Microsoft.ML.Trainers
 {
     /// <include file='doc.xml' path='doc/members/member[@name="SDCA"]/*' />
-    public sealed class SdcaRegressionTrainer : SdcaTrainerBase<SdcaRegressionTrainer.Arguments, RegressionPredictionTransformer<LinearRegressionModelParameters>, LinearRegressionModelParameters>
+    public sealed class SdcaRegressionTrainer : SdcaTrainerBase<SdcaRegressionTrainer.Options, RegressionPredictionTransformer<LinearRegressionModelParameters>, LinearRegressionModelParameters>
     {
         internal const string LoadNameValue = "SDCAR";
         internal const string UserNameValue = "Fast Linear Regression (SA-SDCA)";
         internal const string ShortName = "sasdcar";
         internal const string Summary = "The SDCA linear regression trainer.";
 
-        public sealed class Arguments : ArgumentsBase
+        public sealed class Options : ArgumentsBase
         {
             [Argument(ArgumentType.Multiple, HelpText = "Loss Function", ShortName = "loss", SortOrder = 50)]
             public ISupportSdcaRegressionLossFactory LossFunction = new SquaredLossFactory();
 
-            public Arguments()
+            public Options()
             {
                 // Using a higher default tolerance for better RMS.
                 ConvergenceTolerance = 0.01f;
@@ -61,21 +61,16 @@ public Arguments()
         /// <param name="l2Const">The L2 regularization hyperparameter.</param>
         /// <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
         /// <param name="maxIterations">The maximum number of passes to perform over the data.</param>
-        /// <param name="advancedSettings">A delegate to set more settings.
-        /// The settings here will override the ones provided in the direct method signature,
-        /// if both are present and have different values.
-        /// The columns names, however need to be provided directly, not through the <paramref name="advancedSettings"/>.</param>
-        public SdcaRegressionTrainer(IHostEnvironment env,
+        internal SdcaRegressionTrainer(IHostEnvironment env,
             string labelColumn = DefaultColumnNames.Label,
             string featureColumn = DefaultColumnNames.Features,
             string weights = null,
             ISupportSdcaRegressionLoss loss = null,
             float? l2Const = null,
             float? l1Threshold = null,
-            int? maxIterations = null,
-            Action<Arguments> advancedSettings = null)
-             : base(env, featureColumn, TrainerUtils.MakeR4ScalarColumn(labelColumn), TrainerUtils.MakeR4ScalarWeightColumn(weights), advancedSettings,
-                  l2Const, l1Threshold, maxIterations)
+            int? maxIterations = null)
+             : base(env, featureColumn, TrainerUtils.MakeR4ScalarColumn(labelColumn), TrainerUtils.MakeR4ScalarWeightColumn(weights),
+                   l2Const, l1Threshold, maxIterations)
         {
             Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
             Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
@@ -83,18 +78,18 @@ public SdcaRegressionTrainer(IHostEnvironment env,
             Loss = _loss;
         }
 
-        internal SdcaRegressionTrainer(IHostEnvironment env, Arguments args, string featureColumn, string labelColumn, string weightColumn = null)
-            : base(env, args, TrainerUtils.MakeR4ScalarColumn(labelColumn), TrainerUtils.MakeR4ScalarWeightColumn(weightColumn))
+        internal SdcaRegressionTrainer(IHostEnvironment env, Options options, string featureColumn, string labelColumn, string weightColumn = null)
+            : base(env, options, TrainerUtils.MakeR4ScalarColumn(labelColumn), TrainerUtils.MakeR4ScalarWeightColumn(weightColumn))
         {
             Host.CheckValue(labelColumn, nameof(labelColumn));
             Host.CheckValue(featureColumn, nameof(featureColumn));
 
-            _loss = args.LossFunction.CreateComponent(env);
+            _loss = options.LossFunction.CreateComponent(env);
             Loss = _loss;
         }
 
-        internal SdcaRegressionTrainer(IHostEnvironment env, Arguments args)
-            : this(env, args, args.FeatureColumn, args.LabelColumn)
+        internal SdcaRegressionTrainer(IHostEnvironment env, Options options)
+            : this(env, options, options.FeatureColumn, options.LabelColumn)
         {
         }
 
@@ -178,14 +173,14 @@ public static partial class Sdca
             ShortName = SdcaRegressionTrainer.ShortName,
             XmlInclude = new[] { @"<include file='../Microsoft.ML.StandardLearners/Standard/doc.xml' path='doc/members/member[@name=""SDCA""]/*' />",
                                  @"<include file='../Microsoft.ML.StandardLearners/Standard/doc.xml' path='doc/members/example[@name=""StochasticDualCoordinateAscentRegressor""]/*' />" })]
-        public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, SdcaRegressionTrainer.Arguments input)
+        public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, SdcaRegressionTrainer.Options input)
         {
             Contracts.CheckValue(env, nameof(env));
             var host = env.Register("TrainSDCA");
             host.CheckValue(input, nameof(input));
             EntryPointUtils.CheckInputArgs(host, input);
 
-            return LearnerEntryPointsUtils.Train<SdcaRegressionTrainer.Arguments, CommonOutputs.RegressionOutput>(host, input,
+            return LearnerEntryPointsUtils.Train<SdcaRegressionTrainer.Options, CommonOutputs.RegressionOutput>(host, input,
                 () => new SdcaRegressionTrainer(host, input),
                 () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn));
         }
diff --git a/src/Microsoft.ML.StandardLearners/StandardLearnersCatalog.cs b/src/Microsoft.ML.StandardLearners/StandardLearnersCatalog.cs
index c5018748f8..2b62f817e0 100644
--- a/src/Microsoft.ML.StandardLearners/StandardLearnersCatalog.cs
+++ b/src/Microsoft.ML.StandardLearners/StandardLearnersCatalog.cs
@@ -13,7 +13,7 @@
 namespace Microsoft.ML
 {
     using LRArguments = LogisticRegression.Arguments;
-    using SgdArguments = StochasticGradientDescentClassificationTrainer.Arguments;
+    using SgdOptions = StochasticGradientDescentClassificationTrainer.Options;
 
     /// <summary>
     /// TrainerEstimator extension methods.
@@ -31,20 +31,33 @@ public static class StandardLearnersCatalog
         /// <param name="initLearningRate">The initial learning rate used by SGD.</param>
         /// <param name="l2Weight">The L2 regularization constant.</param>
         /// <param name="loss">The loss function to use.</param>
-        /// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
         public static StochasticGradientDescentClassificationTrainer StochasticGradientDescent(this BinaryClassificationContext.BinaryClassificationTrainers ctx,
             string labelColumn = DefaultColumnNames.Label,
             string featureColumn = DefaultColumnNames.Features,
             string weights = null,
-            int maxIterations = SgdArguments.Defaults.MaxIterations,
-            double initLearningRate = SgdArguments.Defaults.InitLearningRate,
-            float l2Weight = SgdArguments.Defaults.L2Weight,
-            ISupportClassificationLossFactory loss = null,
-            Action<SgdArguments> advancedSettings = null)
+            int maxIterations = SgdOptions.Defaults.MaxIterations,
+            double initLearningRate = SgdOptions.Defaults.InitLearningRate,
+            float l2Weight = SgdOptions.Defaults.L2Weight,
+            ISupportClassificationLossFactory loss = null)
+        {
+            Contracts.CheckValue(ctx, nameof(ctx));
+            var env = CatalogUtils.GetEnvironment(ctx);
+            return new StochasticGradientDescentClassificationTrainer(env, labelColumn, featureColumn, weights, maxIterations, initLearningRate, l2Weight, loss);
+        }
+
+        /// <summary>
+        ///  Predict a target using a linear binary classification model trained with the <see cref="StochasticGradientDescentClassificationTrainer"/> trainer.
+        /// </summary>
+        /// <param name="ctx">The binary classificaiton context trainer object.</param>
+        /// <param name="options">Advanced arguments to the algorithm.</param>
+        public static StochasticGradientDescentClassificationTrainer StochasticGradientDescent(this BinaryClassificationContext.BinaryClassificationTrainers ctx,
+            SgdOptions options)
         {
             Contracts.CheckValue(ctx, nameof(ctx));
+            Contracts.CheckValue(options, nameof(options));
+
             var env = CatalogUtils.GetEnvironment(ctx);
-            return new StochasticGradientDescentClassificationTrainer(env, labelColumn, featureColumn, weights, maxIterations, initLearningRate, l2Weight, loss, advancedSettings);
+            return new StochasticGradientDescentClassificationTrainer(env, options);
         }
 
         /// <summary>
@@ -58,10 +71,6 @@ public static StochasticGradientDescentClassificationTrainer StochasticGradientD
         /// <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
         /// <param name="maxIterations">The maximum number of passes to perform over the data.</param>
         /// <param name="loss">The custom loss, if unspecified will be <see cref="SquaredLoss"/>.</param>
-        /// <param name="advancedSettings">A delegate to set more settings.
-        /// The settings here will override the ones provided in the direct method signature,
-        /// if both are present and have different values.
-        /// The columns names, however need to be provided directly, not through the <paramref name="advancedSettings"/>.</param>
         public static SdcaRegressionTrainer StochasticDualCoordinateAscent(this RegressionContext.RegressionTrainers ctx,
             string labelColumn = DefaultColumnNames.Label,
             string featureColumn = DefaultColumnNames.Features,
@@ -69,12 +78,26 @@ public static SdcaRegressionTrainer StochasticDualCoordinateAscent(this Regressi
             ISupportSdcaRegressionLoss loss = null,
             float? l2Const = null,
             float? l1Threshold = null,
-            int? maxIterations = null,
-            Action<SdcaRegressionTrainer.Arguments> advancedSettings = null)
+            int? maxIterations = null)
         {
             Contracts.CheckValue(ctx, nameof(ctx));
             var env = CatalogUtils.GetEnvironment(ctx);
-            return new SdcaRegressionTrainer(env, labelColumn, featureColumn, weights, loss, l2Const, l1Threshold, maxIterations, advancedSettings);
+            return new SdcaRegressionTrainer(env, labelColumn, featureColumn, weights, loss, l2Const, l1Threshold, maxIterations);
+        }
+
+        /// <summary>
+        /// Predict a target using a linear regression model trained with the SDCA trainer.
+        /// </summary>
+        /// <param name="ctx">The regression context trainer object.</param>
+        /// <param name="options">Advanced arguments to the algorithm.</param>
+        public static SdcaRegressionTrainer StochasticDualCoordinateAscent(this RegressionContext.RegressionTrainers ctx,
+            SdcaRegressionTrainer.Options options)
+        {
+            Contracts.CheckValue(ctx, nameof(ctx));
+            Contracts.CheckValue(options, nameof(options));
+
+            var env = CatalogUtils.GetEnvironment(ctx);
+            return new SdcaRegressionTrainer(env, options);
         }
 
         /// <summary>
@@ -88,16 +111,6 @@ public static SdcaRegressionTrainer StochasticDualCoordinateAscent(this Regressi
         /// <param name="l2Const">The L2 regularization hyperparameter.</param>
         /// <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
         /// <param name="maxIterations">The maximum number of passes to perform over the data.</param>
-        /// <param name="advancedSettings">A delegate to set more settings.
-        /// The settings here will override the ones provided in the direct method signature,
-        /// if both are present and have different values.
-        /// The columns names, however need to be provided directly, not through the <paramref name="advancedSettings"/>.</param>
-        /// <example>
-        /// <format type="text/markdown">
-        /// <![CDATA[
-        ///  [!code-csharp[SDCA](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/SDCA.cs)]
-        /// ]]></format>
-        /// </example>
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[
@@ -112,13 +125,27 @@ public static SdcaBinaryTrainer StochasticDualCoordinateAscent(
                 ISupportSdcaClassificationLoss loss = null,
                 float? l2Const = null,
                 float? l1Threshold = null,
-                int? maxIterations = null,
-                Action<SdcaBinaryTrainer.Arguments> advancedSettings = null
-            )
+                int? maxIterations = null)
+        {
+            Contracts.CheckValue(ctx, nameof(ctx));
+            var env = CatalogUtils.GetEnvironment(ctx);
+            return new SdcaBinaryTrainer(env, labelColumn, featureColumn, weights, loss, l2Const, l1Threshold, maxIterations);
+        }
+
+        /// <summary>
+        /// Predict a target using a linear binary classification model trained with the SDCA trainer.
+        /// </summary>
+        /// <param name="ctx">The binary classification context trainer object.</param>
+        /// <param name="options">Advanced arguments to the algorithm.</param>
+        public static SdcaBinaryTrainer StochasticDualCoordinateAscent(
+                this BinaryClassificationContext.BinaryClassificationTrainers ctx,
+                SdcaBinaryTrainer.Options options)
         {
             Contracts.CheckValue(ctx, nameof(ctx));
+            Contracts.CheckValue(options, nameof(options));
+
             var env = CatalogUtils.GetEnvironment(ctx);
-            return new SdcaBinaryTrainer(env, labelColumn, featureColumn, weights, loss, l2Const, l1Threshold, maxIterations, advancedSettings);
+            return new SdcaBinaryTrainer(env, options);
         }
 
         /// <summary>
@@ -132,10 +159,6 @@ public static SdcaBinaryTrainer StochasticDualCoordinateAscent(
         /// <param name="l2Const">The L2 regularization hyperparameter.</param>
         /// <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
         /// <param name="maxIterations">The maximum number of passes to perform over the data.</param>
-        /// <param name="advancedSettings">A delegate to set more settings.
-        /// The settings here will override the ones provided in the direct method signature,
-        /// if both are present and have different values.
-        /// The columns names, however need to be provided directly, not through the <paramref name="advancedSettings"/>.</param>
         public static SdcaMultiClassTrainer StochasticDualCoordinateAscent(this MulticlassClassificationContext.MulticlassClassificationTrainers ctx,
                     string labelColumn = DefaultColumnNames.Label,
                     string featureColumn = DefaultColumnNames.Features,
@@ -143,12 +166,26 @@ public static SdcaMultiClassTrainer StochasticDualCoordinateAscent(this Multicla
                     ISupportSdcaClassificationLoss loss = null,
                     float? l2Const = null,
                     float? l1Threshold = null,
-                    int? maxIterations = null,
-                    Action<SdcaMultiClassTrainer.Arguments> advancedSettings = null)
+                    int? maxIterations = null)
         {
             Contracts.CheckValue(ctx, nameof(ctx));
             var env = CatalogUtils.GetEnvironment(ctx);
-            return new SdcaMultiClassTrainer(env, labelColumn, featureColumn, weights, loss, l2Const, l1Threshold, maxIterations, advancedSettings);
+            return new SdcaMultiClassTrainer(env, labelColumn, featureColumn, weights, loss, l2Const, l1Threshold, maxIterations);
+        }
+
+        /// <summary>
+        /// Predict a target using a linear multiclass classification model trained with the SDCA trainer.
+        /// </summary>
+        /// <param name="ctx">The multiclass classification context trainer object.</param>
+        /// <param name="options">Advanced arguments to the algorithm.</param>
+        public static SdcaMultiClassTrainer StochasticDualCoordinateAscent(this MulticlassClassificationContext.MulticlassClassificationTrainers ctx,
+                    SdcaMultiClassTrainer.Options options)
+        {
+            Contracts.CheckValue(ctx, nameof(ctx));
+            Contracts.CheckValue(options, nameof(options));
+
+            var env = CatalogUtils.GetEnvironment(ctx);
+            return new SdcaMultiClassTrainer(env, options);
         }
 
         /// <summary>
@@ -177,6 +214,7 @@ public static AveragedPerceptronTrainer AveragedPerceptron(
             Action<AveragedPerceptronTrainer.Arguments> advancedSettings = null)
         {
             Contracts.CheckValue(ctx, nameof(ctx));
+
             var env = CatalogUtils.GetEnvironment(ctx);
             return new AveragedPerceptronTrainer(env, labelColumn, featureColumn, weights, lossFunction ?? new LogLoss(), learningRate, decreaseLearningRate, l2RegularizerWeight, numIterations, advancedSettings);
         }
diff --git a/src/Microsoft.ML.StaticPipe/SdcaStaticExtensions.cs b/src/Microsoft.ML.StaticPipe/SdcaStaticExtensions.cs
index 8743d8e77c..0754484c5b 100644
--- a/src/Microsoft.ML.StaticPipe/SdcaStaticExtensions.cs
+++ b/src/Microsoft.ML.StaticPipe/SdcaStaticExtensions.cs
@@ -26,10 +26,6 @@ public static class SdcaStaticExtensions
         /// <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
         /// <param name="maxIterations">The maximum number of passes to perform over the data.</param>
         /// <param name="loss">The custom loss, if unspecified will be <see cref="SquaredLoss"/>.</param>
-        /// <param name="advancedSettings">A delegate to set more settings.
-        /// The settings here will override the ones provided in the direct method signature,
-        /// if both are present and have different values.
-        /// The columns names, however need to be provided directly, not through the <paramref name="advancedSettings"/>.</param>
         /// <param name="onFit">A delegate that is called every time the
         /// <see cref="Estimator{TInShape, TShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
         /// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
@@ -48,7 +44,6 @@ public static Scalar<float> Sdca(this RegressionContext.RegressionTrainers ctx,
             float? l1Threshold = null,
             int? maxIterations = null,
             ISupportSdcaRegressionLoss loss = null,
-            Action<SdcaRegressionTrainer.Arguments> advancedSettings = null,
             Action<LinearRegressionModelParameters> onFit = null)
         {
             Contracts.CheckValue(label, nameof(label));
@@ -58,13 +53,58 @@ public static Scalar<float> Sdca(this RegressionContext.RegressionTrainers ctx,
             Contracts.CheckParam(!(l1Threshold < 0), nameof(l1Threshold), "Must not be negative, if specified.");
             Contracts.CheckParam(!(maxIterations < 1), nameof(maxIterations), "Must be positive if specified");
             Contracts.CheckValueOrNull(loss);
-            Contracts.CheckValueOrNull(advancedSettings);
             Contracts.CheckValueOrNull(onFit);
 
             var rec = new TrainerEstimatorReconciler.Regression(
                 (env, labelName, featuresName, weightsName) =>
                 {
-                    var trainer = new SdcaRegressionTrainer(env, labelName, featuresName, weightsName, loss, l2Const, l1Threshold, maxIterations, advancedSettings);
+                    var trainer = new SdcaRegressionTrainer(env, labelName, featuresName, weightsName, loss, l2Const, l1Threshold, maxIterations);
+                    if (onFit != null)
+                        return trainer.WithOnFitDelegate(trans => onFit(trans.Model));
+                    return trainer;
+                }, label, features, weights);
+
+            return rec.Score;
+        }
+
+        /// <summary>
+        /// Predict a target using a linear regression model trained with the SDCA trainer.
+        /// </summary>
+        /// <param name="ctx">The regression context trainer object.</param>
+        /// <param name="label">The label, or dependent variable.</param>
+        /// <param name="features">The features, or independent variables.</param>
+        /// <param name="weights">The optional example weights.</param>
+        /// <param name="options">Advanced arguments to the algorithm.</param>
+        /// <param name="onFit">A delegate that is called every time the
+        /// <see cref="Estimator{TInShape, TShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
+        /// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
+        /// the linear model that was trained.  Note that this action cannot change the result in any way; it is only a way for the caller to
+        /// be informed about what was learnt.</param>
+        /// <returns>The predicted output.</returns>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        ///  [!code-csharp[SDCA](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Static/SDCARegression.cs)]
+        /// ]]></format>
+        /// </example>
+        public static Scalar<float> Sdca(this RegressionContext.RegressionTrainers ctx,
+            Scalar<float> label, Vector<float> features, Scalar<float> weights,
+            SdcaRegressionTrainer.Options options,
+            Action<LinearRegressionModelParameters> onFit = null)
+        {
+            Contracts.CheckValue(label, nameof(label));
+            Contracts.CheckValue(features, nameof(features));
+            Contracts.CheckValueOrNull(weights);
+            Contracts.CheckValueOrNull(options);
+            Contracts.CheckValueOrNull(onFit);
+
+            var rec = new TrainerEstimatorReconciler.Regression(
+                (env, labelName, featuresName, weightsName) =>
+                {
+                    options.LabelColumn = labelName;
+                    options.FeatureColumn = featuresName;
+
+                    var trainer = new SdcaRegressionTrainer(env, options);
                     if (onFit != null)
                         return trainer.WithOnFitDelegate(trans => onFit(trans.Model));
                     return trainer;
@@ -83,10 +123,6 @@ public static Scalar<float> Sdca(this RegressionContext.RegressionTrainers ctx,
         /// <param name="l2Const">The L2 regularization hyperparameter.</param>
         /// <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
         /// <param name="maxIterations">The maximum number of passes to perform over the data.</param>
-        /// <param name="advancedSettings">A delegate to set more settings.
-        /// The settings here will override the ones provided in the direct method signature,
-        /// if both are present and have different values.
-        /// The columns names, however need to be provided directly, not through the <paramref name="advancedSettings"/>.</param>
         /// <param name="onFit">A delegate that is called every time the
         /// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
         /// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
@@ -106,7 +142,6 @@ public static (Scalar<float> score, Scalar<float> probability, Scalar<bool> pred
                     float? l2Const = null,
                     float? l1Threshold = null,
                     int? maxIterations = null,
-                    Action<SdcaBinaryTrainer.Arguments> advancedSettings = null,
                     Action<LinearBinaryModelParameters, ParameterMixingCalibratedPredictor> onFit = null)
         {
             Contracts.CheckValue(label, nameof(label));
@@ -115,13 +150,69 @@ public static (Scalar<float> score, Scalar<float> probability, Scalar<bool> pred
             Contracts.CheckParam(!(l2Const < 0), nameof(l2Const), "Must not be negative, if specified.");
             Contracts.CheckParam(!(l1Threshold < 0), nameof(l1Threshold), "Must not be negative, if specified.");
             Contracts.CheckParam(!(maxIterations < 1), nameof(maxIterations), "Must be positive if specified");
-            Contracts.CheckValueOrNull(advancedSettings);
             Contracts.CheckValueOrNull(onFit);
 
             var rec = new TrainerEstimatorReconciler.BinaryClassifier(
                 (env, labelName, featuresName, weightsName) =>
                 {
-                    var trainer = new SdcaBinaryTrainer(env, labelName, featuresName, weightsName, loss: new LogLoss(), l2Const, l1Threshold, maxIterations, advancedSettings);
+                    var trainer = new SdcaBinaryTrainer(env, labelName, featuresName, weightsName, loss: new LogLoss(), l2Const, l1Threshold, maxIterations);
+                    if (onFit != null)
+                    {
+                        return trainer.WithOnFitDelegate(trans =>
+                        {
+                            // Under the default log-loss we assume a calibrated predictor.
+                            var model = trans.Model;
+                            var cali = (ParameterMixingCalibratedPredictor)model;
+                            var pred = (LinearBinaryModelParameters)cali.SubPredictor;
+                            onFit(pred, cali);
+                        });
+                    }
+                    return trainer;
+                }, label, features, weights);
+
+            return rec.Output;
+        }
+
+        /// <summary>
+        /// Predict a target using a linear binary classification model trained with the SDCA trainer, and log-loss.
+        /// </summary>
+        /// <param name="ctx">The binary classification context trainer object.</param>
+        /// <param name="label">The label, or dependent variable.</param>
+        /// <param name="features">The features, or independent variables.</param>
+        /// <param name="weights">The optional example weights.</param>
+        /// <param name="options">Advanced arguments to the algorithm.</param>
+        /// <param name="onFit">A delegate that is called every time the
+        /// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
+        /// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
+        /// the linear model that was trained, as well as the calibrator on top of that model. Note that this action cannot change the
+        /// result in any way; it is only a way for the caller to be informed about what was learnt.</param>
+        /// <returns>The set of output columns including in order the predicted binary classification score (which will range
+        /// from negative to positive infinity), the calibrated prediction (from 0 to 1), and the predicted label.</returns>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        ///  [!code-csharp[SDCA](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Static/SDCABinaryClassification.cs)]
+        /// ]]></format>
+        /// </example>
+        public static (Scalar<float> score, Scalar<float> probability, Scalar<bool> predictedLabel) Sdca(
+                    this BinaryClassificationContext.BinaryClassificationTrainers ctx,
+                    Scalar<bool> label, Vector<float> features, Scalar<float> weights,
+                    SdcaBinaryTrainer.Options options,
+                    Action<LinearBinaryModelParameters, ParameterMixingCalibratedPredictor> onFit = null)
+        {
+            Contracts.CheckValue(label, nameof(label));
+            Contracts.CheckValue(features, nameof(features));
+            Contracts.CheckValueOrNull(weights);
+            Contracts.CheckValueOrNull(options);
+            Contracts.CheckValueOrNull(onFit);
+
+            var rec = new TrainerEstimatorReconciler.BinaryClassifier(
+                (env, labelName, featuresName, weightsName) =>
+                {
+                    options.LabelColumn = labelName;
+                    options.FeatureColumn = featuresName;
+
+                    var trainer = new SdcaBinaryTrainer(env, options);
                     if (onFit != null)
                     {
                         return trainer.WithOnFitDelegate(trans =>
@@ -152,10 +243,6 @@ public static (Scalar<float> score, Scalar<float> probability, Scalar<bool> pred
         /// <param name="l2Const">The L2 regularization hyperparameter.</param>
         /// <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
         /// <param name="maxIterations">The maximum number of passes to perform over the data.</param>
-        /// <param name="advancedSettings">A delegate to set more settings.
-        /// The settings here will override the ones provided in the direct method signature,
-        /// if both are present and have different values.
-        /// The columns names, however need to be provided directly, not through the <paramref name="advancedSettings"/>.</param>
         /// <param name="onFit">A delegate that is called every time the
         /// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
         /// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
@@ -163,7 +250,6 @@ public static (Scalar<float> score, Scalar<float> probability, Scalar<bool> pred
         /// result in any way; it is only a way for the caller to be informed about what was learnt.</param>
         /// <returns>The set of output columns including in order the predicted binary classification score (which will range
         /// from negative to positive infinity), and the predicted label.</returns>
-        /// <seealso cref="Sdca(BinaryClassificationContext.BinaryClassificationTrainers, Scalar{bool}, Vector{float}, Scalar{float}, float?, float?, int?, Action{SdcaBinaryTrainer.Arguments}, Action{LinearBinaryModelParameters, ParameterMixingCalibratedPredictor})"/>
         public static (Scalar<float> score, Scalar<bool> predictedLabel) Sdca(
                 this BinaryClassificationContext.BinaryClassificationTrainers ctx,
                 Scalar<bool> label, Vector<float> features,
@@ -172,7 +258,6 @@ public static (Scalar<float> score, Scalar<bool> predictedLabel) Sdca(
                 float? l2Const = null,
                 float? l1Threshold = null,
                 int? maxIterations = null,
-                Action<SdcaBinaryTrainer.Arguments> advancedSettings = null,
                 Action<LinearBinaryModelParameters> onFit = null
             )
         {
@@ -183,7 +268,6 @@ public static (Scalar<float> score, Scalar<bool> predictedLabel) Sdca(
             Contracts.CheckParam(!(l2Const < 0), nameof(l2Const), "Must not be negative, if specified.");
             Contracts.CheckParam(!(l1Threshold < 0), nameof(l1Threshold), "Must not be negative, if specified.");
             Contracts.CheckParam(!(maxIterations < 1), nameof(maxIterations), "Must be positive if specified");
-            Contracts.CheckValueOrNull(advancedSettings);
             Contracts.CheckValueOrNull(onFit);
 
             bool hasProbs = loss is LogLoss;
@@ -191,7 +275,66 @@ public static (Scalar<float> score, Scalar<bool> predictedLabel) Sdca(
             var rec = new TrainerEstimatorReconciler.BinaryClassifierNoCalibration(
                 (env, labelName, featuresName, weightsName) =>
                 {
-                    var trainer = new SdcaBinaryTrainer(env, labelName, featuresName, weightsName, loss, l2Const, l1Threshold, maxIterations, advancedSettings);
+                    var trainer = new SdcaBinaryTrainer(env, labelName, featuresName, weightsName, loss, l2Const, l1Threshold, maxIterations);
+                    if (onFit != null)
+                    {
+                        return trainer.WithOnFitDelegate(trans =>
+                        {
+                            var model = trans.Model;
+                            if (model is ParameterMixingCalibratedPredictor cali)
+                                onFit((LinearBinaryModelParameters)cali.SubPredictor);
+                            else
+                                onFit((LinearBinaryModelParameters)model);
+                        });
+                    }
+                    return trainer;
+                }, label, features, weights, hasProbs);
+
+            return rec.Output;
+        }
+
+        /// <summary>
+        /// Predict a target using a linear binary classification model trained with the SDCA trainer, and a custom loss.
+        /// Note that because we cannot be sure that all loss functions will produce naturally calibrated outputs, setting
+        /// a custom loss function will not produce a calibrated probability column.
+        /// </summary>
+        /// <param name="ctx">The binary classification context trainer object.</param>
+        /// <param name="label">The label, or dependent variable.</param>
+        /// <param name="features">The features, or independent variables.</param>
+        /// <param name="loss">The custom loss.</param>
+        /// <param name="weights">The optional example weights.</param>
+        /// <param name="options">Advanced arguments to the algorithm.</param>
+        /// <param name="onFit">A delegate that is called every time the
+        /// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
+        /// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
+        /// the linear model that was trained, as well as the calibrator on top of that model. Note that this action cannot change the
+        /// result in any way; it is only a way for the caller to be informed about what was learnt.</param>
+        /// <returns>The set of output columns including in order the predicted binary classification score (which will range
+        /// from negative to positive infinity), and the predicted label.</returns>
+        public static (Scalar<float> score, Scalar<bool> predictedLabel) Sdca(
+                this BinaryClassificationContext.BinaryClassificationTrainers ctx,
+                Scalar<bool> label, Vector<float> features,
+                Scalar<float> weights,
+                ISupportSdcaClassificationLoss loss,
+                SdcaBinaryTrainer.Options options,
+                Action<LinearBinaryModelParameters> onFit = null
+            )
+        {
+            Contracts.CheckValue(label, nameof(label));
+            Contracts.CheckValue(features, nameof(features));
+            Contracts.CheckValueOrNull(weights);
+            Contracts.CheckValueOrNull(options);
+            Contracts.CheckValueOrNull(onFit);
+
+            bool hasProbs = loss is LogLoss;
+
+            var rec = new TrainerEstimatorReconciler.BinaryClassifierNoCalibration(
+                (env, labelName, featuresName, weightsName) =>
+                {
+                    options.FeatureColumn = featuresName;
+                    options.LabelColumn = labelName;
+
+                    var trainer = new SdcaBinaryTrainer(env, options);
                     if (onFit != null)
                     {
                         return trainer.WithOnFitDelegate(trans =>
@@ -220,10 +363,6 @@ public static (Scalar<float> score, Scalar<bool> predictedLabel) Sdca(
         /// <param name="l2Const">The L2 regularization hyperparameter.</param>
         /// <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
         /// <param name="maxIterations">The maximum number of passes to perform over the data.</param>
-        /// <param name="advancedSettings">A delegate to set more settings.
-        /// The settings here will override the ones provided in the direct method signature,
-        /// if both are present and have different values.
-        /// The columns names, however need to be provided directly, not through the <paramref name="advancedSettings"/>.</param>
         /// <param name="onFit">A delegate that is called every time the
         /// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
         /// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
@@ -239,7 +378,6 @@ public static (Vector<float> score, Key<uint, TVal> predictedLabel)
                     float? l2Const = null,
                     float? l1Threshold = null,
                     int? maxIterations = null,
-                    Action<SdcaMultiClassTrainer.Arguments> advancedSettings = null,
                     Action<MulticlassLogisticRegressionModelParameters> onFit = null)
         {
             Contracts.CheckValue(label, nameof(label));
@@ -249,13 +387,55 @@ public static (Vector<float> score, Key<uint, TVal> predictedLabel)
             Contracts.CheckParam(!(l2Const < 0), nameof(l2Const), "Must not be negative, if specified.");
             Contracts.CheckParam(!(l1Threshold < 0), nameof(l1Threshold), "Must not be negative, if specified.");
             Contracts.CheckParam(!(maxIterations < 1), nameof(maxIterations), "Must be positive if specified");
-            Contracts.CheckValueOrNull(advancedSettings);
             Contracts.CheckValueOrNull(onFit);
 
             var rec = new TrainerEstimatorReconciler.MulticlassClassifier<TVal>(
                 (env, labelName, featuresName, weightsName) =>
                 {
-                    var trainer = new SdcaMultiClassTrainer(env, labelName, featuresName, weightsName, loss, l2Const, l1Threshold, maxIterations, advancedSettings);
+                    var trainer = new SdcaMultiClassTrainer(env, labelName, featuresName, weightsName, loss, l2Const, l1Threshold, maxIterations);
+                    if (onFit != null)
+                        return trainer.WithOnFitDelegate(trans => onFit(trans.Model));
+                    return trainer;
+                }, label, features, weights);
+
+            return rec.Output;
+        }
+
+        /// <summary>
+        /// Predict a target using a linear multiclass classification model trained with the SDCA trainer.
+        /// </summary>
+        /// <param name="ctx">The multiclass classification context trainer object.</param>
+        /// <param name="label">The label, or dependent variable.</param>
+        /// <param name="features">The features, or independent variables.</param>
+        /// <param name="weights">The optional example weights.</param>
+        /// <param name="options">Advanced arguments to the algorithm.</param>
+        /// <param name="onFit">A delegate that is called every time the
+        /// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
+        /// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
+        /// the linear model that was trained. Note that this action cannot change the
+        /// result in any way; it is only a way for the caller to be informed about what was learnt.</param>
+        /// <returns>The set of output columns including in order the predicted per-class likelihoods (between 0 and 1, and summing up to 1), and the predicted label.</returns>
+        public static (Vector<float> score, Key<uint, TVal> predictedLabel)
+                Sdca<TVal>(this MulticlassClassificationContext.MulticlassClassificationTrainers ctx,
+                    Key<uint, TVal> label,
+                    Vector<float> features,
+                    Scalar<float> weights,
+                    SdcaMultiClassTrainer.Options options,
+                    Action<MulticlassLogisticRegressionModelParameters> onFit = null)
+        {
+            Contracts.CheckValue(label, nameof(label));
+            Contracts.CheckValue(features, nameof(features));
+            Contracts.CheckValueOrNull(weights);
+            Contracts.CheckValueOrNull(options);
+            Contracts.CheckValueOrNull(onFit);
+
+            var rec = new TrainerEstimatorReconciler.MulticlassClassifier<TVal>(
+                (env, labelName, featuresName, weightsName) =>
+                {
+                    options.LabelColumn = labelName;
+                    options.FeatureColumn = featuresName;
+
+                    var trainer = new SdcaMultiClassTrainer(env, options);
                     if (onFit != null)
                         return trainer.WithOnFitDelegate(trans => onFit(trans.Model));
                     return trainer;
diff --git a/src/Microsoft.ML.StaticPipe/SgdStatic.cs b/src/Microsoft.ML.StaticPipe/SgdStatic.cs
index 5e5bd98d08..e5c579cc12 100644
--- a/src/Microsoft.ML.StaticPipe/SgdStatic.cs
+++ b/src/Microsoft.ML.StaticPipe/SgdStatic.cs
@@ -3,13 +3,15 @@
 // See the LICENSE file in the project root for more information.
 
 using System;
+using Microsoft.ML.Data;
+using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.StaticPipe.Runtime;
 using Microsoft.ML.Trainers;
 
 namespace Microsoft.ML.StaticPipe
 {
-    using Arguments = StochasticGradientDescentClassificationTrainer.Arguments;
+    using Options = StochasticGradientDescentClassificationTrainer.Options;
 
     /// <summary>
     /// Binary Classification trainer estimators.
@@ -27,7 +29,6 @@ public static class SgdStaticExtensions
         /// <param name="initLearningRate">The initial learning rate used by SGD.</param>
         /// <param name="l2Weight">The L2 regularization constant.</param>
         /// <param name="loss">The loss function to use.</param>
-        /// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
         /// <param name="onFit">A delegate that is called every time the
         /// <see cref="Estimator{TTupleInShape, TTupleOutShape, TTransformer}.Fit(DataView{TTupleInShape})"/> method is called on the
         /// <see cref="Estimator{TTupleInShape, TTupleOutShape, TTransformer}"/> instance created out of this. This delegate will receive
@@ -38,17 +39,55 @@ public static (Scalar<float> score, Scalar<float> probability, Scalar<bool> pred
             Scalar<bool> label,
             Vector<float> features,
             Scalar<float> weights = null,
-            int maxIterations = Arguments.Defaults.MaxIterations,
-            double initLearningRate = Arguments.Defaults.InitLearningRate,
-            float l2Weight = Arguments.Defaults.L2Weight,
+            int maxIterations = Options.Defaults.MaxIterations,
+            double initLearningRate = Options.Defaults.InitLearningRate,
+            float l2Weight = Options.Defaults.L2Weight,
             ISupportClassificationLossFactory loss = null,
-            Action<Arguments> advancedSettings = null,
             Action<IPredictorWithFeatureWeights<float>> onFit = null)
         {
             var rec = new TrainerEstimatorReconciler.BinaryClassifier(
                 (env, labelName, featuresName, weightsName) =>
                 {
-                    var trainer = new StochasticGradientDescentClassificationTrainer(env, labelName, featuresName, weightsName, maxIterations, initLearningRate, l2Weight, loss, advancedSettings);
+                    var trainer = new StochasticGradientDescentClassificationTrainer(env, labelName, featuresName, weightsName, maxIterations, initLearningRate, l2Weight, loss);
+
+                    if (onFit != null)
+                        return trainer.WithOnFitDelegate(trans => onFit(trans.Model));
+                    return trainer;
+
+                }, label, features, weights);
+
+            return rec.Output;
+        }
+
+        /// <summary>
+        ///  Predict a target using a linear binary classification model trained with the <see cref="Microsoft.ML.Trainers.StochasticGradientDescentClassificationTrainer"/> trainer.
+        /// </summary>
+        /// <param name="ctx">The binary classificaiton context trainer object.</param>
+        /// <param name="label">The name of the label column.</param>
+        /// <param name="features">The name of the feature column.</param>
+        /// <param name="weights">The name for the example weight column.</param>
+        /// <param name="options">Advanced arguments to the algorithm.</param>
+        /// <param name="onFit">A delegate that is called every time the
+        /// <see cref="Estimator{TTupleInShape, TTupleOutShape, TTransformer}.Fit(DataView{TTupleInShape})"/> method is called on the
+        /// <see cref="Estimator{TTupleInShape, TTupleOutShape, TTransformer}"/> instance created out of this. This delegate will receive
+        /// the linear model that was trained.  Note that this action cannot change the result in any way; it is only a way for the caller to
+        /// be informed about what was learnt.</param>
+        /// <returns>The predicted output.</returns>
+        public static (Scalar<float> score, Scalar<float> probability, Scalar<bool> predictedLabel) StochasticGradientDescentClassificationTrainer(this BinaryClassificationContext.BinaryClassificationTrainers ctx,
+            Scalar<bool> label,
+            Vector<float> features,
+            Scalar<float> weights,
+            Options options,
+            Action<IPredictorWithFeatureWeights<float>> onFit = null)
+        {
+            var rec = new TrainerEstimatorReconciler.BinaryClassifier(
+                (env, labelName, featuresName, weightsName) =>
+                {
+                    options.FeatureColumn = featuresName;
+                    options.LabelColumn = labelName;
+                    options.WeightColumn = weightsName != null ? Optional<string>.Explicit(weightsName) : Optional<string>.Implicit(DefaultColumnNames.Weight);
+
+                    var trainer = new StochasticGradientDescentClassificationTrainer(env, options);
 
                     if (onFit != null)
                         return trainer.WithOnFitDelegate(trans => onFit(trans.Model));
diff --git a/src/Microsoft.ML.StaticPipe/TreeTrainersStatic.cs b/src/Microsoft.ML.StaticPipe/TreeTrainersStatic.cs
index 7bac497fb9..ba0a8456c0 100644
--- a/src/Microsoft.ML.StaticPipe/TreeTrainersStatic.cs
+++ b/src/Microsoft.ML.StaticPipe/TreeTrainersStatic.cs
@@ -3,6 +3,8 @@
 // See the LICENSE file in the project root for more information.
 
 using System;
+using Microsoft.ML.Data;
+using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.StaticPipe.Runtime;
 using Microsoft.ML.Trainers.FastTree;
@@ -93,6 +95,10 @@ public static Scalar<float> FastTree(this RegressionContext.RegressionTrainers c
             var rec = new TrainerEstimatorReconciler.Regression(
                (env, labelName, featuresName, weightsName) =>
                {
+                   options.LabelColumn = labelName;
+                   options.FeatureColumn = featuresName;
+                   options.WeightColumn = weightsName != null ? Optional<string>.Explicit(weightsName) : Optional<string>.Implicit(DefaultColumnNames.Weight);
+
                    var trainer = new FastTreeRegressionTrainer(env, options);
                    if (onFit != null)
                        return trainer.WithOnFitDelegate(trans => onFit(trans.Model));
@@ -185,6 +191,10 @@ public static (Scalar<float> score, Scalar<float> probability, Scalar<bool> pred
             var rec = new TrainerEstimatorReconciler.BinaryClassifier(
                (env, labelName, featuresName, weightsName) =>
                {
+                   options.LabelColumn = labelName;
+                   options.FeatureColumn = featuresName;
+                   options.WeightColumn = weightsName != null ? Optional<string>.Explicit(weightsName) : Optional<string>.Implicit(DefaultColumnNames.Weight);
+
                    var trainer = new FastTreeBinaryClassificationTrainer(env, options);
 
                    if (onFit != null)
@@ -265,6 +275,11 @@ public static Scalar<float> FastTree<TVal>(this RankingContext.RankingTrainers c
             var rec = new TrainerEstimatorReconciler.Ranker<TVal>(
                (env, labelName, featuresName, groupIdName, weightsName) =>
                {
+                   options.LabelColumn = labelName;
+                   options.FeatureColumn = featuresName;
+                   options.GroupIdColumn = groupIdName;
+                   options.WeightColumn = weightsName != null ? Optional<string>.Explicit(weightsName) : Optional<string>.Implicit(DefaultColumnNames.Weight);
+
                    var trainer = new FastTreeRankingTrainer(env, options);
                    if (onFit != null)
                        return trainer.WithOnFitDelegate(trans => onFit(trans.Model));
diff --git a/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv b/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv
index 74d896997f..948aeafd9a 100644
--- a/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv
+++ b/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv
@@ -65,10 +65,10 @@ Trainers.OnlineGradientDescentRegressor	Train a Online gradient descent perceptr
 Trainers.OrdinaryLeastSquaresRegressor	Train an OLS regression model.	Microsoft.ML.Trainers.HalLearners.OlsLinearRegressionTrainer	TrainRegression	Microsoft.ML.Trainers.HalLearners.OlsLinearRegressionTrainer+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
 Trainers.PcaAnomalyDetector	Train an PCA Anomaly model.	Microsoft.ML.Trainers.PCA.RandomizedPcaTrainer	TrainPcaAnomaly	Microsoft.ML.Trainers.PCA.RandomizedPcaTrainer+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+AnomalyDetectionOutput
 Trainers.PoissonRegressor	Train an Poisson regression model.	Microsoft.ML.Trainers.PoissonRegression	TrainRegression	Microsoft.ML.Trainers.PoissonRegression+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
-Trainers.StochasticDualCoordinateAscentBinaryClassifier	Train an SDCA binary model.	Microsoft.ML.Trainers.Sdca	TrainBinary	Microsoft.ML.Trainers.SdcaBinaryTrainer+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
-Trainers.StochasticDualCoordinateAscentClassifier	The SDCA linear multi-class classification trainer.	Microsoft.ML.Trainers.Sdca	TrainMultiClass	Microsoft.ML.Trainers.SdcaMultiClassTrainer+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput
-Trainers.StochasticDualCoordinateAscentRegressor	The SDCA linear regression trainer.	Microsoft.ML.Trainers.Sdca	TrainRegression	Microsoft.ML.Trainers.SdcaRegressionTrainer+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
-Trainers.StochasticGradientDescentBinaryClassifier	Train an Hogwild SGD binary model.	Microsoft.ML.Trainers.StochasticGradientDescentClassificationTrainer	TrainBinary	Microsoft.ML.Trainers.StochasticGradientDescentClassificationTrainer+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
+Trainers.StochasticDualCoordinateAscentBinaryClassifier	Train an SDCA binary model.	Microsoft.ML.Trainers.Sdca	TrainBinary	Microsoft.ML.Trainers.SdcaBinaryTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
+Trainers.StochasticDualCoordinateAscentClassifier	The SDCA linear multi-class classification trainer.	Microsoft.ML.Trainers.Sdca	TrainMultiClass	Microsoft.ML.Trainers.SdcaMultiClassTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput
+Trainers.StochasticDualCoordinateAscentRegressor	The SDCA linear regression trainer.	Microsoft.ML.Trainers.Sdca	TrainRegression	Microsoft.ML.Trainers.SdcaRegressionTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
+Trainers.StochasticGradientDescentBinaryClassifier	Train an Hogwild SGD binary model.	Microsoft.ML.Trainers.StochasticGradientDescentClassificationTrainer	TrainBinary	Microsoft.ML.Trainers.StochasticGradientDescentClassificationTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
 Trainers.SymSgdBinaryClassifier	Train a symbolic SGD.	Microsoft.ML.Trainers.SymSgd.SymSgdClassificationTrainer	TrainSymSgd	Microsoft.ML.Trainers.SymSgd.SymSgdClassificationTrainer+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
 Transforms.ApproximateBootstrapSampler	Approximate bootstrap sampling.	Microsoft.ML.Transforms.BootstrapSample	GetSample	Microsoft.ML.Transforms.BootstrapSamplingTransformer+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+TransformOutput
 Transforms.BinaryPredictionScoreColumnsRenamer	For binary prediction, it renames the PredictedLabel and Score columns to include the name of the positive class.	Microsoft.ML.EntryPoints.ScoreModel	RenameBinaryPredictionScoreColumns	Microsoft.ML.EntryPoints.ScoreModel+RenameBinaryPredictionScoreColumnsInput	Microsoft.ML.EntryPoints.CommonOutputs+TransformOutput
diff --git a/test/Microsoft.ML.Benchmarks/PredictionEngineBench.cs b/test/Microsoft.ML.Benchmarks/PredictionEngineBench.cs
index f0269f4d7c..76d3d2aea4 100644
--- a/test/Microsoft.ML.Benchmarks/PredictionEngineBench.cs
+++ b/test/Microsoft.ML.Benchmarks/PredictionEngineBench.cs
@@ -53,7 +53,8 @@ public void SetupIrisPipeline()
             IDataView data = reader.Read(_irisDataPath);
 
             var pipeline = new ColumnConcatenatingEstimator(env, "Features", new[] { "SepalLength", "SepalWidth", "PetalLength", "PetalWidth" })
-                .Append(new SdcaMultiClassTrainer(env, "Label", "Features", advancedSettings: (s) => { s.NumThreads = 1; s.ConvergenceTolerance = 1e-2f; }));
+                .Append(env.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(
+                    new SdcaMultiClassTrainer.Options {NumThreads = 1, ConvergenceTolerance = 1e-2f, }));
 
             var model = pipeline.Fit(data);
 
@@ -82,7 +83,8 @@ public void SetupSentimentPipeline()
             IDataView data = reader.Read(_sentimentDataPath);
 
             var pipeline = new TextFeaturizingEstimator(env, "SentimentText", "Features")
-                .Append(new SdcaBinaryTrainer(env, "Label", "Features", advancedSettings: (s) => { s.NumThreads = 1; s.ConvergenceTolerance = 1e-2f; }));
+                .Append(env.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+                    new SdcaBinaryTrainer.Options {NumThreads = 1, ConvergenceTolerance = 1e-2f, }));
 
             var model = pipeline.Fit(data);
 
@@ -110,7 +112,8 @@ public void SetupBreastCancerPipeline()
 
             IDataView data = reader.Read(_breastCancerDataPath);
 
-            var pipeline = new SdcaBinaryTrainer(env, "Label", "Features", advancedSettings: (s) => { s.NumThreads = 1; s.ConvergenceTolerance = 1e-2f; });
+            var pipeline = env.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+                new SdcaBinaryTrainer.Options { NumThreads = 1, ConvergenceTolerance = 1e-2f, });
 
             var model = pipeline.Fit(data);
 
diff --git a/test/Microsoft.ML.Benchmarks/StochasticDualCoordinateAscentClassifierBench.cs b/test/Microsoft.ML.Benchmarks/StochasticDualCoordinateAscentClassifierBench.cs
index 7e8dcd97c5..afbc5e1d4f 100644
--- a/test/Microsoft.ML.Benchmarks/StochasticDualCoordinateAscentClassifierBench.cs
+++ b/test/Microsoft.ML.Benchmarks/StochasticDualCoordinateAscentClassifierBench.cs
@@ -68,7 +68,7 @@ private TransformerChain<MulticlassPredictionTransformer<MulticlassLogisticRegre
             IDataView data = reader.Read(dataPath);
 
             var pipeline = new ColumnConcatenatingEstimator(_env, "Features", new[] { "SepalLength", "SepalWidth", "PetalLength", "PetalWidth" })
-                .Append(new SdcaMultiClassTrainer(_env, "Label", "Features"));
+                .Append(_env.MulticlassClassification.Trainers.StochasticDualCoordinateAscent());
 
             return pipeline.Fit(data);
         }
@@ -112,7 +112,7 @@ public void TrainSentiment()
             var trans = new WordEmbeddingsExtractingEstimator(_env, "WordEmbeddings_TransformedText", "Features",
                 WordEmbeddingsExtractingTransformer.PretrainedModelKind.Sswe).Fit(text).Transform(text);
             // Train
-            var trainer = new SdcaMultiClassTrainer(_env, "Label", "Features", maxIterations: 20);
+            var trainer = _env.MulticlassClassification.Trainers.StochasticDualCoordinateAscent();
             var predicted = trainer.Fit(trans);
             _consumer.Consume(predicted);
         }
diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs b/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs
index 4660a8d2a4..921e831514 100644
--- a/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs
+++ b/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs
@@ -2488,7 +2488,7 @@ public void TestInputBuilderComponentFactories()
             Assert.True(success);
             var inputBuilder = new InputBuilder(Env, info.InputType, catalog);
 
-            var args = new SdcaBinaryTrainer.Arguments()
+            var args = new SdcaBinaryTrainer.Options()
             {
                 NormalizeFeatures = NormalizeOption.Yes,
                 CheckFrequency = 42
diff --git a/test/Microsoft.ML.StaticPipelineTesting/Training.cs b/test/Microsoft.ML.StaticPipelineTesting/Training.cs
index 01d0ba4cc5..237ab3e25a 100644
--- a/test/Microsoft.ML.StaticPipelineTesting/Training.cs
+++ b/test/Microsoft.ML.StaticPipelineTesting/Training.cs
@@ -47,8 +47,9 @@ public void SdcaRegression()
             LinearRegressionModelParameters pred = null;
 
             var est = reader.MakeNewEstimator()
-                .Append(r => (r.label, score: ctx.Trainers.Sdca(r.label, r.features, maxIterations: 2,
-                onFit: p => pred = p, advancedSettings: s => s.NumThreads = 1)));
+                .Append(r => (r.label, score: ctx.Trainers.Sdca(r.label, r.features, null,
+                new SdcaRegressionTrainer.Options() { MaxIterations = 2, NumThreads = 1 },
+                onFit: p => pred = p)));
 
             var pipe = reader.Append(est);
 
@@ -88,7 +89,8 @@ public void SdcaRegressionNameCollision()
                 separator: ';', hasHeader: true);
 
             var est = reader.MakeNewEstimator()
-                .Append(r => (r.label, r.Score, score: ctx.Trainers.Sdca(r.label, r.features, maxIterations: 2, advancedSettings: s => s.NumThreads = 1)));
+                .Append(r => (r.label, r.Score, score: ctx.Trainers.Sdca(r.label, r.features, null,
+                new SdcaRegressionTrainer.Options() { MaxIterations = 2, NumThreads = 1 })));
 
             var pipe = reader.Append(est);
 
@@ -119,10 +121,9 @@ public void SdcaBinaryClassification()
             ParameterMixingCalibratedPredictor cali = null;
 
             var est = reader.MakeNewEstimator()
-                .Append(r => (r.label, preds: ctx.Trainers.Sdca(r.label, r.features,
-                    maxIterations: 2,
-                    onFit: (p, c) => { pred = p; cali = c; },
-                    advancedSettings: s => s.NumThreads = 1)));
+                .Append(r => (r.label, preds: ctx.Trainers.Sdca(r.label, r.features, null,
+                    new SdcaBinaryTrainer.Options { MaxIterations = 2, NumThreads = 1 },
+                    onFit: (p, c) => { pred = p; cali = c; })));
 
             var pipe = reader.Append(est);
 
@@ -167,10 +168,9 @@ public void SdcaBinaryClassificationNoCalibration()
 
             // With a custom loss function we no longer get calibrated predictions.
             var est = reader.MakeNewEstimator()
-                .Append(r => (r.label, preds: ctx.Trainers.Sdca(r.label, r.features,
-                maxIterations: 2,
-                loss: loss, onFit: p => pred = p,
-                advancedSettings: s => s.NumThreads = 1)));
+                .Append(r => (r.label, preds: ctx.Trainers.Sdca(r.label, r.features, null, loss,
+                new SdcaBinaryTrainer.Options { MaxIterations = 2, NumThreads = 1 },
+                onFit: p => pred = p)));
 
             var pipe = reader.Append(est);
 
@@ -951,10 +951,9 @@ public void HogwildSGDBinaryClassification()
             IPredictorWithFeatureWeights<float> pred = null;
 
             var est = reader.MakeNewEstimator()
-                .Append(r => (r.label, preds: ctx.Trainers.StochasticGradientDescentClassificationTrainer(r.label, r.features,
-                    l2Weight: 0,
-                    onFit: (p) => { pred = p; },
-                    advancedSettings: s => s.NumThreads = 1)));
+                .Append(r => (r.label, preds: ctx.Trainers.StochasticGradientDescentClassificationTrainer(r.label, r.features, null,
+                    new StochasticGradientDescentClassificationTrainer.Options { L2Weight = 0, NumThreads = 1 },
+                    onFit: (p) => { pred = p; })));
 
             var pipe = reader.Append(est);
 
@@ -1105,8 +1104,8 @@ public void MultiClassLightGbmStaticPipelineWithInMemoryData()
 
             // The predicted label below should be  with probability 0.922597349.
             Console.WriteLine("Our predicted label to this example is {0} with probability {1}",
-                nativeLabels[(int)nativePrediction.PredictedLabelIndex-1],
-                nativePrediction.Scores[(int)nativePrediction.PredictedLabelIndex-1]);
+                nativeLabels[(int)nativePrediction.PredictedLabelIndex - 1],
+                nativePrediction.Scores[(int)nativePrediction.PredictedLabelIndex - 1]);
         }
     }
 }
\ No newline at end of file
diff --git a/test/Microsoft.ML.Tests/FeatureContributionTests.cs b/test/Microsoft.ML.Tests/FeatureContributionTests.cs
index e41cf6954d..cac36a2e31 100644
--- a/test/Microsoft.ML.Tests/FeatureContributionTests.cs
+++ b/test/Microsoft.ML.Tests/FeatureContributionTests.cs
@@ -9,6 +9,7 @@
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.RunTests;
+using Microsoft.ML.Trainers;
 using Microsoft.ML.Training;
 using Microsoft.ML.Transforms;
 using Xunit;
@@ -72,7 +73,8 @@ public void TestFastTreeTweedieRegression()
         [Fact]
         public void TestSDCARegression()
         {
-            TestFeatureContribution(ML.Regression.Trainers.StochasticDualCoordinateAscent(advancedSettings: args => { args.NumThreads = 1; }), GetSparseDataset(numberOfInstances: 100), "SDCARegression");
+            TestFeatureContribution(ML.Regression.Trainers.StochasticDualCoordinateAscent(
+                new SdcaRegressionTrainer.Options { NumThreads = 1, }), GetSparseDataset(numberOfInstances: 100), "SDCARegression");
         }
 
         [Fact]
@@ -146,13 +148,16 @@ public void TestLightGbmBinary()
         [Fact]
         public void TestSDCABinary()
         {
-            TestFeatureContribution(ML.BinaryClassification.Trainers.StochasticDualCoordinateAscent(advancedSettings: args => { args.NumThreads = 1; }), GetSparseDataset(TaskType.BinaryClassification, 100), "SDCABinary");
+            TestFeatureContribution(ML.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+                new SdcaBinaryTrainer.Options { NumThreads = 1, }), GetSparseDataset(TaskType.BinaryClassification, 100), "SDCABinary");
         }
 
         [Fact]
         public void TestSGDBinary()
         {
-            TestFeatureContribution(ML.BinaryClassification.Trainers.StochasticGradientDescent(advancedSettings: args => { args.NumThreads = 1; }), GetSparseDataset(TaskType.BinaryClassification, 100), "SGDBinary");
+            TestFeatureContribution(ML.BinaryClassification.Trainers.StochasticGradientDescent(
+                new StochasticGradientDescentClassificationTrainer.Options { NumThreads = 1}), 
+                GetSparseDataset(TaskType.BinaryClassification, 100), "SGDBinary");
         }
 
         [Fact]
diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/CrossValidation.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/CrossValidation.cs
index 6016cfbdb0..11b34438e6 100644
--- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/CrossValidation.cs
+++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/CrossValidation.cs
@@ -3,6 +3,7 @@
 // See the LICENSE file in the project root for more information.
 
 using Microsoft.ML.RunTests;
+using Microsoft.ML.Trainers;
 using Xunit;
 
 namespace Microsoft.ML.Tests.Scenarios.Api
@@ -26,7 +27,8 @@ void CrossValidation()
 
             // Pipeline.
             var pipeline = ml.Transforms.Text.FeaturizeText("SentimentText", "Features")
-                    .Append(ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features", advancedSettings: (s) => { s.ConvergenceTolerance = 1f; s.NumThreads = 1; }));
+                    .Append(ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+                        new SdcaBinaryTrainer.Options { ConvergenceTolerance = 1f, NumThreads = 1, }));
 
             var cvResult = ml.BinaryClassification.CrossValidate(data, pipeline);
         }
diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/DecomposableTrainAndPredict.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/DecomposableTrainAndPredict.cs
index 94f53e65f2..3d8164e353 100644
--- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/DecomposableTrainAndPredict.cs
+++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/DecomposableTrainAndPredict.cs
@@ -5,6 +5,7 @@
 using System.Linq;
 using Microsoft.ML.Data;
 using Microsoft.ML.RunTests;
+using Microsoft.ML.Trainers;
 using Microsoft.ML.Transforms;
 using Microsoft.ML.Transforms.Conversions;
 using Xunit;
@@ -31,7 +32,8 @@ void DecomposableTrainAndPredict()
 
             var pipeline = new ColumnConcatenatingEstimator (ml, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")
                 .Append(new ValueToKeyMappingEstimator(ml, "Label"), TransformerScope.TrainTest)
-                .Append(ml.MulticlassClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features",advancedSettings: s => { s.MaxIterations = 100; s.Shuffle = true; s.NumThreads = 1; }))
+                .Append(ml.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(
+                    new SdcaMultiClassTrainer.Options { MaxIterations = 100, Shuffle = true, NumThreads = 1, }))
                 .Append(new KeyToValueMappingEstimator(ml, "PredictedLabel"));
 
             var model = pipeline.Fit(data).GetModelFor(TransformerScope.Scoring);
diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Evaluation.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Evaluation.cs
index 5d890cf7b8..c5bbce6f12 100644
--- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Evaluation.cs
+++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Evaluation.cs
@@ -4,6 +4,7 @@
 
 using Microsoft.ML.Data;
 using Microsoft.ML.RunTests;
+using Microsoft.ML.Trainers;
 using Xunit;
 
 namespace Microsoft.ML.Tests.Scenarios.Api
@@ -24,7 +25,8 @@ public void Evaluation()
             // Pipeline.
             var pipeline = ml.Data.CreateTextReader(TestDatasets.Sentiment.GetLoaderColumns(), hasHeader: true)
                 .Append(ml.Transforms.Text.FeaturizeText("SentimentText", "Features"))
-                .Append(ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features", advancedSettings: s => s.NumThreads = 1));
+                .Append(ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+                    new SdcaBinaryTrainer.Options { NumThreads = 1 }));
 
             // Train.
             var readerModel = pipeline.Fit(new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename)));
diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Extensibility.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Extensibility.cs
index 84bd6691e9..94160d8caa 100644
--- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Extensibility.cs
+++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Extensibility.cs
@@ -6,6 +6,7 @@
 using System.Linq;
 using Microsoft.ML.Data;
 using Microsoft.ML.RunTests;
+using Microsoft.ML.Trainers;
 using Microsoft.ML.Transforms;
 using Microsoft.ML.Transforms.Conversions;
 using Xunit;
@@ -40,7 +41,8 @@ void Extensibility()
             var pipeline = new ColumnConcatenatingEstimator (ml, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")
                 .Append(new CustomMappingEstimator<IrisData, IrisData>(ml, action, null), TransformerScope.TrainTest)
                 .Append(new ValueToKeyMappingEstimator(ml, "Label"), TransformerScope.TrainTest)
-                .Append(ml.MulticlassClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features", advancedSettings: (s) => { s.MaxIterations = 100; s.Shuffle = true; s.NumThreads = 1; }))
+                .Append(ml.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(
+                    new SdcaMultiClassTrainer.Options { MaxIterations = 100, Shuffle = true, NumThreads = 1 }))
                 .Append(new KeyToValueMappingEstimator(ml, "PredictedLabel"));
 
             var model = pipeline.Fit(data).GetModelFor(TransformerScope.Scoring);
diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/FileBasedSavingOfData.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/FileBasedSavingOfData.cs
index afae98455c..e27d6360a0 100644
--- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/FileBasedSavingOfData.cs
+++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/FileBasedSavingOfData.cs
@@ -6,6 +6,7 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.Data.IO;
 using Microsoft.ML.RunTests;
+using Microsoft.ML.Trainers;
 using Xunit;
 
 namespace Microsoft.ML.Tests.Scenarios.Api
@@ -37,7 +38,8 @@ void FileBasedSavingOfData()
                     DataSaverUtils.SaveDataView(ch, saver, trainData, file);
             }
 
-            var trainer = ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features", advancedSettings: s => s.NumThreads = 1);
+            var trainer = ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+                new SdcaBinaryTrainer.Options { NumThreads = 1 });
             var loadedTrainData = new BinaryLoader(ml, new BinaryLoader.Arguments(), new MultiFileSource(path));
 
             // Train.
diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/IntrospectiveTraining.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/IntrospectiveTraining.cs
index 023cff8d76..eae28ef7f4 100644
--- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/IntrospectiveTraining.cs
+++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/IntrospectiveTraining.cs
@@ -4,6 +4,7 @@
 
 using Microsoft.ML.Data;
 using Microsoft.ML.RunTests;
+using Microsoft.ML.Trainers;
 using Xunit;
 
 namespace Microsoft.ML.Tests.Scenarios.Api
@@ -32,7 +33,8 @@ public void IntrospectiveTraining()
 
             var pipeline = ml.Transforms.Text.FeaturizeText("SentimentText", "Features")
                 .AppendCacheCheckpoint(ml)
-                .Append(ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features", advancedSettings: s => s.NumThreads = 1));
+                .Append(ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+                    new SdcaBinaryTrainer.Options { NumThreads = 1 }));
 
             // Train.
             var model = pipeline.Fit(data);
@@ -40,7 +42,6 @@ public void IntrospectiveTraining()
             // Get feature weights.
             VBuffer<float> weights = default;
             model.LastTransformer.Model.GetFeatureWeights(ref weights);
-
         }
     }
 }
diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Metacomponents.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Metacomponents.cs
index 70b6b0bbb5..b3c33095d0 100644
--- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Metacomponents.cs
+++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Metacomponents.cs
@@ -24,7 +24,8 @@ public void Metacomponents()
             var ml = new MLContext();
             var data = ml.Data.ReadFromTextFile<IrisData>(GetDataPath(TestDatasets.irisData.trainFilename), separatorChar: ',');
 
-            var sdcaTrainer = ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features", advancedSettings: (s) => { s.MaxIterations = 100; s.Shuffle = true; s.NumThreads = 1; });
+            var sdcaTrainer = ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+                new SdcaBinaryTrainer.Options { MaxIterations = 100, Shuffle = true, NumThreads = 1, });
 
             var pipeline = new ColumnConcatenatingEstimator (ml, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")
                 .Append(new ValueToKeyMappingEstimator(ml, "Label"), TransformerScope.TrainTest)
diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/MultithreadedPrediction.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/MultithreadedPrediction.cs
index e710956462..cb6074578d 100644
--- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/MultithreadedPrediction.cs
+++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/MultithreadedPrediction.cs
@@ -5,6 +5,7 @@
 using System.Threading.Tasks;
 using Microsoft.ML.Data;
 using Microsoft.ML.RunTests;
+using Microsoft.ML.Trainers;
 using Xunit;
 
 namespace Microsoft.ML.Tests.Scenarios.Api
@@ -29,7 +30,8 @@ void MultithreadedPrediction()
             // Pipeline.
             var pipeline = ml.Transforms.Text.FeaturizeText("SentimentText", "Features")
                 .AppendCacheCheckpoint(ml)
-                .Append(ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features", advancedSettings: s => s.NumThreads = 1));
+                .Append(ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+                    new SdcaBinaryTrainer.Options { NumThreads = 1 }));
 
             // Train.
             var model = pipeline.Fit(data);
diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/ReconfigurablePrediction.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/ReconfigurablePrediction.cs
index 5b9482ae6e..e6d4592a84 100644
--- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/ReconfigurablePrediction.cs
+++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/ReconfigurablePrediction.cs
@@ -4,6 +4,7 @@
 
 using Microsoft.ML.Data;
 using Microsoft.ML.RunTests;
+using Microsoft.ML.Trainers;
 using Xunit;
 
 namespace Microsoft.ML.Tests.Scenarios.Api
@@ -29,7 +30,9 @@ public void ReconfigurablePrediction()
             var pipeline = ml.Transforms.Text.FeaturizeText("SentimentText", "Features")
                 .Fit(data);
 
-            var trainer = ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features", advancedSettings: (s) => s.NumThreads = 1);
+            var trainer = ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+                new SdcaBinaryTrainer.Options { NumThreads = 1 });
+
             var trainData = ml.Data.Cache(pipeline.Transform(data)); // Cache the data right before the trainer to boost the training speed.
             var model = trainer.Fit(trainData);
 
diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/SimpleTrainAndPredict.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/SimpleTrainAndPredict.cs
index af2c7ffa99..838c840f5b 100644
--- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/SimpleTrainAndPredict.cs
+++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/SimpleTrainAndPredict.cs
@@ -5,6 +5,7 @@
 using System.Linq;
 using Microsoft.ML.Data;
 using Microsoft.ML.RunTests;
+using Microsoft.ML.Trainers;
 using Xunit;
 
 namespace Microsoft.ML.Tests.Scenarios.Api
@@ -26,7 +27,8 @@ public void SimpleTrainAndPredict()
             // Pipeline.
             var pipeline = ml.Transforms.Text.FeaturizeText("SentimentText", "Features")
                 .AppendCacheCheckpoint(ml)
-                .Append(ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features", advancedSettings: s => s.NumThreads = 1));
+                .Append(ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+                    new SdcaBinaryTrainer.Options { NumThreads = 1 }));
 
             // Train.
             var model = pipeline.Fit(data);
diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainSaveModelAndPredict.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainSaveModelAndPredict.cs
index d9cfa732a9..8375a59665 100644
--- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainSaveModelAndPredict.cs
+++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainSaveModelAndPredict.cs
@@ -7,6 +7,7 @@
 using Microsoft.ML.Core.Data;
 using Microsoft.ML.Data;
 using Microsoft.ML.RunTests;
+using Microsoft.ML.Trainers;
 using Xunit;
 
 namespace Microsoft.ML.Tests.Scenarios.Api
@@ -28,7 +29,8 @@ public void TrainSaveModelAndPredict()
             // Pipeline.
             var pipeline = ml.Transforms.Text.FeaturizeText("SentimentText", "Features")
                 .AppendCacheCheckpoint(ml)
-                .Append(ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features", advancedSettings: s => s.NumThreads = 1));
+                .Append(ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+                    new SdcaBinaryTrainer.Options { NumThreads = 1 }));
 
             // Train.
             var model = pipeline.Fit(data);
diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainWithInitialPredictor.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainWithInitialPredictor.cs
index 4954ecea4a..cac3c7f9be 100644
--- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainWithInitialPredictor.cs
+++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainWithInitialPredictor.cs
@@ -4,6 +4,7 @@
 
 using Microsoft.ML.Data;
 using Microsoft.ML.RunTests;
+using Microsoft.ML.Trainers;
 using Xunit;
 
 namespace Microsoft.ML.Tests.Scenarios.Api
@@ -30,7 +31,9 @@ public void TrainWithInitialPredictor()
             var trainData = ml.Data.Cache(pipeline.Fit(data).Transform(data));
 
             // Train the first predictor.
-            var trainer = ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features",advancedSettings: s => s.NumThreads = 1);
+            var trainer = ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+                new SdcaBinaryTrainer.Options { NumThreads = 1 });
+
             var firstModel = trainer.Fit(trainData);
 
             // Train the second predictor on the same data.
diff --git a/test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationTests.cs b/test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationTests.cs
index f3906ca806..5ebbb6e03a 100644
--- a/test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationTests.cs
+++ b/test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationTests.cs
@@ -5,6 +5,7 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.TestFramework;
+using Microsoft.ML.Trainers;
 using Xunit;
 using Xunit.Abstractions;
 
@@ -30,7 +31,8 @@ public void TrainAndPredictIrisModelTest()
             var pipe = mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")
                             .Append(mlContext.Transforms.Normalize("Features"))
                             .AppendCacheCheckpoint(mlContext)
-                            .Append(mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features", advancedSettings: s => s.NumThreads = 1));
+                            .Append(mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(
+                                new SdcaMultiClassTrainer.Options { NumThreads = 1 }));
 
             // Read training and test data sets
             string dataPath = GetDataPath(TestDatasets.iris.trainFilename);
diff --git a/test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationWithStringLabelTests.cs b/test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationWithStringLabelTests.cs
index ff38fbebe5..2b4c989e4c 100644
--- a/test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationWithStringLabelTests.cs
+++ b/test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationWithStringLabelTests.cs
@@ -3,6 +3,7 @@
 // See the LICENSE file in the project root for more information.
 
 using Microsoft.ML.Data;
+using Microsoft.ML.Trainers;
 using Xunit;
 
 namespace Microsoft.ML.Scenarios
@@ -36,7 +37,8 @@ public void TrainAndPredictIrisModelWithStringLabelTest()
                 .Append(mlContext.Transforms.Normalize("Features"))
                 .Append(mlContext.Transforms.Conversion.MapValueToKey("IrisPlantType", "Label"), TransformerScope.TrainTest)
                 .AppendCacheCheckpoint(mlContext)
-                .Append(mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features", advancedSettings: s => s.NumThreads = 1))
+                .Append(mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(
+                    new SdcaMultiClassTrainer.Options { NumThreads = 1 }))
                 .Append(mlContext.Transforms.Conversion.MapKeyToValue(("PredictedLabel", "Plant")));
 
             // Train the pipeline
diff --git a/test/Microsoft.ML.Tests/Scenarios/TensorflowTests.cs b/test/Microsoft.ML.Tests/Scenarios/TensorflowTests.cs
index 430ae1c28e..8140220de4 100644
--- a/test/Microsoft.ML.Tests/Scenarios/TensorflowTests.cs
+++ b/test/Microsoft.ML.Tests/Scenarios/TensorflowTests.cs
@@ -41,7 +41,7 @@ public void TensorFlowTransforCifarEndToEndTest()
                     .Append(new ColumnConcatenatingEstimator(mlContext, "Features", "Output"))
                     .Append(new ValueToKeyMappingEstimator(mlContext, "Label"))
                     .AppendCacheCheckpoint(mlContext)
-                    .Append(new SdcaMultiClassTrainer(mlContext));
+                    .Append(mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent());
 
 
             var transformer = pipeEstimator.Fit(data);
diff --git a/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/IrisPlantClassificationTests.cs b/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/IrisPlantClassificationTests.cs
index 646eb7b148..bbafc04150 100644
--- a/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/IrisPlantClassificationTests.cs
+++ b/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/IrisPlantClassificationTests.cs
@@ -4,6 +4,7 @@
 
 using Microsoft.ML.Data;
 using Microsoft.ML.RunTests;
+using Microsoft.ML.Trainers;
 using Xunit;
 
 namespace Microsoft.ML.Scenarios
@@ -28,7 +29,8 @@ public void TrainAndPredictIrisModelUsingDirectInstantiationTest()
             var pipe = mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")
                 .Append(mlContext.Transforms.Normalize("Features"))
                 .AppendCacheCheckpoint(mlContext)
-                .Append(mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features", advancedSettings: s => s.NumThreads = 1));
+                .Append(mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(
+                    new SdcaMultiClassTrainer.Options { NumThreads = 1 }));
 
             // Read training and test data sets
             string dataPath = GetDataPath(TestDatasets.iris.trainFilename);
diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/MetalinearEstimators.cs b/test/Microsoft.ML.Tests/TrainerEstimators/MetalinearEstimators.cs
index 8371e3e415..9ed4676c42 100644
--- a/test/Microsoft.ML.Tests/TrainerEstimators/MetalinearEstimators.cs
+++ b/test/Microsoft.ML.Tests/TrainerEstimators/MetalinearEstimators.cs
@@ -43,7 +43,8 @@ public void OVAWithAllConstructorArgs()
         public void OVAUncalibrated()
         {
             var (pipeline, data) = GetMultiClassPipeline();
-            var sdcaTrainer = new SdcaBinaryTrainer(Env, "Label", "Features", advancedSettings: (s) => { s.MaxIterations = 100; s.Shuffle = true; s.NumThreads = 1; s.Calibrator = null; });
+            var sdcaTrainer = ML.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+                new SdcaBinaryTrainer.Options { MaxIterations = 100, Shuffle = true, NumThreads = 1, Calibrator = null });
 
             pipeline = pipeline.Append(new Ova(Env, sdcaTrainer, useProbabilities: false))
                     .Append(new KeyToValueMappingEstimator(Env, "PredictedLabel"));
@@ -60,7 +61,9 @@ public void Pkpd()
         {
             var (pipeline, data) = GetMultiClassPipeline();
 
-            var sdcaTrainer = new SdcaBinaryTrainer(Env, "Label", "Features", advancedSettings: (s) => { s.MaxIterations = 100; s.Shuffle = true; s.NumThreads = 1; });
+            var sdcaTrainer = ML.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+                new SdcaBinaryTrainer.Options { MaxIterations = 100, Shuffle = true, NumThreads = 1 });
+
             pipeline = pipeline.Append(new Pkpd(Env, sdcaTrainer))
                     .Append(new KeyToValueMappingEstimator(Env, "PredictedLabel"));
 
@@ -74,7 +77,14 @@ public void MetacomponentsFeaturesRenamed()
             var data = new TextLoader(Env, TestDatasets.irisData.GetLoaderColumns(), separatorChar: ',')
                 .Read(GetDataPath(TestDatasets.irisData.trainFilename));
 
-            var sdcaTrainer = new SdcaBinaryTrainer(Env, "Label", "Vars", advancedSettings: (s) => { s.MaxIterations = 100; s.Shuffle = true; s.NumThreads = 1; });
+            var sdcaTrainer = ML.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+                new SdcaBinaryTrainer.Options {
+                    LabelColumn = "Label",
+                    FeatureColumn = "Vars",
+                    MaxIterations = 100,
+                    Shuffle = true,
+                    NumThreads = 1, });
+
             var pipeline = new ColumnConcatenatingEstimator(Env, "Vars", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")
                 .Append(new ValueToKeyMappingEstimator(Env, "Label"), TransformerScope.TrainTest)
                 .Append(new Ova(Env, sdcaTrainer))
diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs b/test/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs
index f32242cedc..bef612edeb 100644
--- a/test/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs
+++ b/test/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs
@@ -18,13 +18,16 @@ public void SdcaWorkout()
             var data = TextLoaderStatic.CreateReader(Env, ctx => (Label: ctx.LoadFloat(0), Features: ctx.LoadFloat(1, 10)))
                 .Read(dataPath).Cache();
 
-            var binaryTrainer = new SdcaBinaryTrainer(Env, "Label", "Features", advancedSettings: (s) => s.ConvergenceTolerance = 1e-2f);
+            var binaryTrainer = ML.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+                new SdcaBinaryTrainer.Options { ConvergenceTolerance = 1e-2f });
             TestEstimatorCore(binaryTrainer, data.AsDynamic);
 
-            var regressionTrainer = new SdcaRegressionTrainer(Env, "Label", "Features", advancedSettings: (s) => s.ConvergenceTolerance = 1e-2f);
+            var regressionTrainer = ML.Regression.Trainers.StochasticDualCoordinateAscent(
+                new SdcaRegressionTrainer.Options { ConvergenceTolerance = 1e-2f });
             TestEstimatorCore(regressionTrainer, data.AsDynamic);
 
-            var mcTrainer = new SdcaMultiClassTrainer(Env, "Label", "Features", advancedSettings: (s) => s.ConvergenceTolerance = 1e-2f);
+            var mcTrainer = ML.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(
+                new SdcaMultiClassTrainer.Options { ConvergenceTolerance = 1e-2f });
             TestEstimatorCore(mcTrainer, data.AsDynamic);
 
             Done();
diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/SymSgdClassificationTests.cs b/test/Microsoft.ML.Tests/TrainerEstimators/SymSgdClassificationTests.cs
index 30dc030aed..53edbc9e33 100644
--- a/test/Microsoft.ML.Tests/TrainerEstimators/SymSgdClassificationTests.cs
+++ b/test/Microsoft.ML.Tests/TrainerEstimators/SymSgdClassificationTests.cs
@@ -32,7 +32,7 @@ public void TestEstimatorSymSgdInitPredictor()
             (var pipe, var dataView) = GetBinaryClassificationPipeline();
             var transformedData = pipe.Fit(dataView).Transform(dataView);
 
-            var initPredictor = new SdcaBinaryTrainer(Env, "Label", "Features").Fit(transformedData);
+            var initPredictor = ML.BinaryClassification.Trainers.StochasticDualCoordinateAscent().Fit(transformedData);
             var data = initPredictor.Transform(transformedData);
 
             var withInitPredictor = new SymSgdClassificationTrainer(Env, "Label", "Features").Train(transformedData, initialPredictor: initPredictor.Model);
diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/TrainerEstimators.cs b/test/Microsoft.ML.Tests/TrainerEstimators/TrainerEstimators.cs
index eef3bd0aeb..6b5cbef050 100644
--- a/test/Microsoft.ML.Tests/TrainerEstimators/TrainerEstimators.cs
+++ b/test/Microsoft.ML.Tests/TrainerEstimators/TrainerEstimators.cs
@@ -89,7 +89,7 @@ public void KMeansEstimator()
         public void TestEstimatorHogwildSGD()
         {
             (IEstimator<ITransformer> pipe, IDataView dataView) = GetBinaryClassificationPipeline();
-            var trainer = new StochasticGradientDescentClassificationTrainer(Env, "Label", "Features");
+            var trainer = ML.BinaryClassification.Trainers.StochasticGradientDescent();
             var pipeWithTrainer = pipe.Append(trainer);
             TestEstimatorCore(pipeWithTrainer, dataView);