RegressionTrainers
FastForest(RegressionTrainers, Options)- FastForest(RegressionTrainers, String, String, String, Int32, Int32, Int32)
- FastTree(RegressionTrainers, Options)
- FastTree(RegressionTrainers, String, String, String, Int32, Int32, Int32, Double)
- FastTree(RegressionTrainers, Scalar<Single>, Vector<Single>, Scalar<Single>, Options, Action<FastTreeRegressionModelParameters>)
- FastTree(RegressionTrainers, Scalar<Single>, Vector<Single>, Scalar<Single>, Int32, Int32, Int32, Double, Action<FastTreeRegressionModelParameters>)
- FastTreeTweedie(RegressionTrainers, Options)
- FastTreeTweedie(RegressionTrainers, String, String, String, Int32, Int32, Int32, Double)
- Gam(RegressionTrainers, Options)
- Gam(RegressionTrainers, String, String, String, Int32, Int32, Double)
- LbfgsPoissonRegression(RegressionTrainers, Options)
- LbfgsPoissonRegression(RegressionTrainers, String, String, String, Single, Single, Single, Int32, Boolean)
- LbfgsPoissonRegression(RegressionTrainers, Scalar<Single>, Vector<Single>, Scalar<Single>, Options, Action<PoissonRegressionModelParameters>)
- LbfgsPoissonRegression(RegressionTrainers, Scalar<Single>, Vector<Single>, Scalar<Single>, Single, Single, Single, Int32, Boolean, Action<PoissonRegressionModelParameters>)
- LightGbm(RegressionTrainers, Options)
- LightGbm(RegressionTrainers, Stream, String)
- LightGbm(RegressionTrainers, String, String, String, Int32?, Int32?, Double?, Int32)
- MatrixFactorization<T>(RegressionTrainers, Scalar<Single>, Key<T>, Key<T>, Options, Action<MatrixFactorizationModelParameters>)
- Ols(RegressionTrainers, Options)
- Ols(RegressionTrainers, String, String, String)
- OnlineGradientDescent(RegressionTrainers, Options)
- OnlineGradientDescent(RegressionTrainers, String, String, IRegressionLoss, Single, Boolean, Single, Int32)
- OnlineGradientDescent(RegressionTrainers, Scalar<Single>, Vector<Single>, Scalar<Single>, Options, Action<LinearRegressionModelParameters>)
- OnlineGradientDescent(RegressionTrainers, Scalar<Single>, Vector<Single>, Scalar<Single>, IRegressionLoss, Single, Boolean, Single, Int32, Action<LinearRegressionModelParameters>)
- Sdca(RegressionTrainers, Options)
- Sdca(RegressionTrainers, String, String, String, ISupportSdcaRegressionLoss, Single?, Single?, Int32?)
- Sdca(RegressionTrainers, Scalar<Single>, Vector<Single>, Scalar<Single>, Options, Action<LinearRegressionModelParameters>)
- Sdca(RegressionTrainers, Scalar<Single>, Vector<Single>, Scalar<Single>, Nullable<Single>, Nullable<Single>, Nullable<Int32>, ISupportSdcaRegressionLoss, Action<LinearRegressionModelParameters>)
- SentenceSimilarity(RegressionTrainers, SentenceSimilarityOptions)
- SentenceSimilarity(RegressionTrainers, String, String, String, String, Int32, Int32, BertArchitecture, IDataView)
net471
namespace Microsoft.ML.StaticPipe
{
public static class LbfgsRegressionStaticExtensions : Object
{
public static Scalar<?> LbfgsPoissonRegression(this RegressionTrainers catalog, Scalar<?> label, Vector<?> features, Scalar<?> weights, Options options, Action<PoissonRegressionModelParameters> onFit = null);
}
}
| .NET | 5.06.07.08.09.0 |
|---|---|
| .NET Core | 2.02.12.23.03.1 |
| .NET Framework | 4.6.14.6.24.74.7.14.7.24.84.8.1 |
| .NET Standard | 2.02.1 |
Information specific to net471 | |
| Assembly | Microsoft.ML.StaticPipe , Version=1.0.0.0, PublicKeyToken=cc7b13ffcd2ddd51 |
| Referencing | Your project needs a package reference to |
| Package | Microsoft.ML.StaticPipe (0.14.0) netstandard2.0 |
| Platform Restrictions | This framework does not have platform annotations. |
- Built-in API
- Package-provided API