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XML documentation for Poisson Regression trainer. #3404

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XML documentation for Poisson Regression trainer.
codemzs Apr 18, 2019
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codemzs Apr 20, 2019
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25 changes: 22 additions & 3 deletions src/Microsoft.ML.StandardTrainers/Standard/PoissonRegression/PoissonRegression.cs
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Expand Up @@ -28,12 +28,30 @@ namespace Microsoft.ML.Trainers
/// The <see cref="IEstimator{TTransformer}"/> for training a Poisson regression model.
/// </summary>
/// <remarks>
/// <a href='https://en.wikipedia.org/wiki/Poisson_regression'>Poisson regression</a> is a parameterized regression method.
/// <format type="text/markdown"><![CDATA[
/// To create this trainer, use [LbfgsPoissonRegression](xref:Microsoft.ML.StandardTrainersCatalog.LbfgsPoissonRegression(Microsoft.ML.RegressionCatalog.RegressionTrainers,System.String,System.String,System.String,System.Single,System.Single,System.Single,System.Int32,System.Boolean))
/// or [LbfgsPoissonRegression(Options)](xref:Microsoft.ML.StandardTrainersCatalog.LbfgsPoissonRegression(Microsoft.ML.RegressionCatalog.RegressionTrainers,Microsoft.ML.Trainers.LbfgsPoissonRegressionTrainer.Options)).
///
/// [!include[io](~/../docs/samples/docs/api-reference/io-columns-regression.md)]
///
/// ### Trainer Characteristics
/// | | |
/// | -- | -- |
/// | Machine learning task | Regression |
/// | Is normalization required? | Yes |
/// | Is caching required? | No |
/// | Required NuGet in addition to Microsoft.ML | None |
///
/// ### Training Algorithm Details
/// [Poisson regression](https://en.wikipedia.org/wiki/Poisson_regression) is a parameterized regression method.
/// It assumes that the log of the conditional mean of the dependent variable follows a linear function of the dependent variables.
/// Assuming that the dependent variable follows a Poisson distribution, the regression parameters can be estimated by maximizing the likelihood of the obtained observations.
/// ]]>
/// </format>
/// </remarks>
/// <seealso cref="StandardTrainersCatalog.LbfgsPoissonRegression(RegressionCatalog.RegressionTrainers, string, string, string, float, float, float, int, bool)"/>
/// <seealso cref="StandardTrainersCatalog.LbfgsPoissonRegression(RegressionCatalog.RegressionTrainers, Options)"/>
/// <seealso cref="StandardTrainersCatalog.LbfgsPoissonRegression(RegressionCatalog.RegressionTrainers, LbfgsPoissonRegressionTrainer.Options)"/>
/// <seealso cref="Options"/>
public sealed class LbfgsPoissonRegressionTrainer : LbfgsTrainerBase<LbfgsPoissonRegressionTrainer.Options, RegressionPredictionTransformer<PoissonRegressionModelParameters>, PoissonRegressionModelParameters>
{
internal const string LoadNameValue = "PoissonRegression";
Expand All @@ -42,7 +60,8 @@ public sealed class LbfgsPoissonRegressionTrainer : LbfgsTrainerBase<LbfgsPoisso
internal const string Summary = "Poisson Regression assumes the unknown function, denoted Y has a Poisson distribution.";

/// <summary>
/// Options for the <see cref="LbfgsPoissonRegressionTrainer"/>.
/// Options for the <see cref="LbfgsPoissonRegressionTrainer"/> as used in
/// [LbfgsPoissonRegression(Options)](xref:Microsoft.ML.StandardTrainersCatalog.LbfgsPoissonRegression(Microsoft.ML.RegressionCatalog.RegressionTrainers,Microsoft.ML.Trainers.LbfgsPoissonRegressionTrainer.Options)).
/// </summary>
public sealed class Options : OptionsBase
{
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8 changes: 4 additions & 4 deletions src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs
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Expand Up @@ -573,11 +573,11 @@ public static LbfgsLogisticRegressionBinaryTrainer LbfgsLogisticRegression(this
}

/// <summary>
/// Predict a target using a linear regression model trained with the <see cref="Microsoft.ML.Trainers.LbfgsPoissonRegressionTrainer"/> trainer.
/// Create <see cref="LbfgsPoissonRegressionTrainer"/>, which predicts a target using a linear regression model.
/// </summary>
/// <param name="catalog">The regression catalog trainer object.</param>
/// <param name="labelColumnName">The name of the label column.</param>
/// <param name="featureColumnName">The name of the feature column.</param>
/// <param name="labelColumnName">The name of the label column. The column data must be <see cref="System.Single"/>.</param>
/// <param name="featureColumnName">The name of the feature column. The column data must be a known-sized vector of <see cref="System.Single"/>.</param>
/// <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
/// <param name="l1Regularization">The L1 <a href='https://en.wikipedia.org/wiki/Regularization_(mathematics)'>regularization</a> hyperparameter. Higher values will tend to lead to more sparse model.</param>
/// <param name="l2Regularization">The L2 weight for <a href='https://en.wikipedia.org/wiki/Regularization_(mathematics)'>regularization</a>.</param>
Expand Down Expand Up @@ -606,7 +606,7 @@ public static LbfgsPoissonRegressionTrainer LbfgsPoissonRegression(this Regressi
}

/// <summary>
/// Predict a target using a linear regression model trained with the <see cref="Microsoft.ML.Trainers.LbfgsPoissonRegressionTrainer"/> and advanced options.
/// Create <see cref="LbfgsPoissonRegressionTrainer"/> using advanced options, which predicts a target using a linear regression model.
/// </summary>
/// <param name="catalog">The regression catalog trainer object.</param>
/// <param name="options">Trainer options.</param>
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