diff --git a/src/Microsoft.ML.Data/Transforms/GenerateNumberTransform.cs b/src/Microsoft.ML.Data/Transforms/GenerateNumberTransform.cs
index 8339893652..4293ba7740 100644
--- a/src/Microsoft.ML.Data/Transforms/GenerateNumberTransform.cs
+++ b/src/Microsoft.ML.Data/Transforms/GenerateNumberTransform.cs
@@ -266,7 +266,7 @@ private static VersionInfo GetVersionInfo()
///
/// Host Environment.
/// Input . This is the output from previous transform or loader.
- /// Name of the output column.
+ /// The name of the output column.
/// Seed to start random number generator.
/// Use an auto-incremented integer starting at zero instead of a random number.
public GenerateNumberTransform(IHostEnvironment env, IDataView input, string name, uint? seed = null, bool useCounter = Defaults.UseCounter)
diff --git a/src/Microsoft.ML.Data/Transforms/LabelConvertTransform.cs b/src/Microsoft.ML.Data/Transforms/LabelConvertTransform.cs
index dd64b994aa..33e94d2140 100644
--- a/src/Microsoft.ML.Data/Transforms/LabelConvertTransform.cs
+++ b/src/Microsoft.ML.Data/Transforms/LabelConvertTransform.cs
@@ -73,8 +73,8 @@ private static VersionInfo GetVersionInfo()
///
/// Host Environment.
/// Input . This is the output from previous transform or loader.
- /// Name of the output column.
- /// Name of the input column. If this is null '' will be used.
+ /// The name of the output column.
+ /// The name of the input column. If this is null '' will be used.
public LabelConvertTransform(IHostEnvironment env, IDataView input, string outputColumnName, string inputColumnName = null)
: this(env, new Arguments() { Columns = new[] { new Column() { Source = inputColumnName ?? outputColumnName, Name = outputColumnName } } }, input)
{
diff --git a/src/Microsoft.ML.Data/Transforms/LabelIndicatorTransform.cs b/src/Microsoft.ML.Data/Transforms/LabelIndicatorTransform.cs
index 864cceb128..76fe36e368 100644
--- a/src/Microsoft.ML.Data/Transforms/LabelIndicatorTransform.cs
+++ b/src/Microsoft.ML.Data/Transforms/LabelIndicatorTransform.cs
@@ -120,8 +120,8 @@ private static string TestIsMulticlassLabel(DataViewType type)
/// Host Environment.
/// Input . This is the output from previous transform or loader.
/// Label of the positive class.
- /// Name of the output column.
- /// Name of the input column. If this is null '' will be used.
+ /// The name of the output column.
+ /// The name of the input column. If this is null '' will be used.
public LabelIndicatorTransform(IHostEnvironment env,
IDataView input,
int classIndex,
diff --git a/src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs b/src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs
index 1d80e9061e..95105df7f4 100644
--- a/src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs
+++ b/src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs
@@ -269,8 +269,8 @@ public sealed class SupervisedBinArguments : BinArgumentsBase
///
/// Host Environment.
/// Input . This is the output from previous transform or loader.
- /// Name of the output column.
- /// Name of the column to be transformed. If this is null '' will be used.
+ /// The name of the output column.
+ /// The name of the column to be transformed. If this is null '' will be used.
public static IDataView CreateMinMaxNormalizer(IHostEnvironment env, IDataView input, string outputColumnName, string inputColumnName = null)
{
Contracts.CheckValue(env, nameof(env));
diff --git a/src/Microsoft.ML.Data/Transforms/RangeFilter.cs b/src/Microsoft.ML.Data/Transforms/RangeFilter.cs
index fb08fc34d2..b64685e7ed 100644
--- a/src/Microsoft.ML.Data/Transforms/RangeFilter.cs
+++ b/src/Microsoft.ML.Data/Transforms/RangeFilter.cs
@@ -86,7 +86,7 @@ private static VersionInfo GetVersionInfo()
///
/// Host Environment.
/// Input . This is the output from previous transform or loader.
- /// Name of the input column.
+ /// The name of the input column.
/// Minimum value (0 to 1 for key types).
/// Maximum value (0 to 1 for key types).
/// Whether to include the upper bound.
diff --git a/src/Microsoft.ML.Data/Transforms/TypeConverting.cs b/src/Microsoft.ML.Data/Transforms/TypeConverting.cs
index f0870602b7..7a39021e02 100644
--- a/src/Microsoft.ML.Data/Transforms/TypeConverting.cs
+++ b/src/Microsoft.ML.Data/Transforms/TypeConverting.cs
@@ -187,8 +187,8 @@ private static (string outputColumnName, string inputColumnName)[] GetColumnPair
/// Convinence constructor for simple one column case.
///
/// Host Environment.
- /// Name of the output column.
- /// Name of the column to be transformed. If this is null '' will be used.
+ /// The name of the output column.
+ /// The name of the column to be transformed. If this is null '' will be used.
/// The expected type of the converted column.
/// New key count if we work with key type.
internal TypeConvertingTransformer(IHostEnvironment env, string outputColumnName, DataKind outputKind, string inputColumnName = null, KeyCount outputKeyCount = null)
diff --git a/src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs b/src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs
index f495baf6d4..dd5f7c8745 100644
--- a/src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs
+++ b/src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs
@@ -17,9 +17,9 @@ public static class TreeExtensions
/// Predict a target using a decision tree regression model trained with the .
///
/// The .
- /// The label column.
- /// The feature column.
- /// The optional weights column.
+ /// The name of the label column.
+ /// The name of the feature column.
+ /// The name of the optional weights column.
/// Total number of decision trees to create in the ensemble.
/// The maximum number of leaves per decision tree.
/// The minimal number of datapoints allowed in a leaf of a regression tree, out of the subsampled data.
@@ -27,7 +27,7 @@ public static class TreeExtensions
public static FastTreeRegressionTrainer FastTree(this RegressionCatalog.RegressionTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
- string weights = null,
+ string weightsColumn = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves,
@@ -35,7 +35,7 @@ public static FastTreeRegressionTrainer FastTree(this RegressionCatalog.Regressi
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
- return new FastTreeRegressionTrainer(env, labelColumn, featureColumn, weights, numLeaves, numTrees, minDatapointsInLeaves, learningRate);
+ return new FastTreeRegressionTrainer(env, labelColumn, featureColumn, weightsColumn, numLeaves, numTrees, minDatapointsInLeaves, learningRate);
}
///
@@ -57,9 +57,9 @@ public static FastTreeRegressionTrainer FastTree(this RegressionCatalog.Regressi
/// Predict a target using a decision tree binary classification model trained with the .
///
/// The .
- /// The labelColumn column.
- /// The featureColumn column.
- /// The optional weights column.
+ /// The name of the label column.
+ /// The name of the feature column.
+ /// The name of the optional weights column.
/// Total number of decision trees to create in the ensemble.
/// The maximum number of leaves per decision tree.
/// The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.
@@ -67,7 +67,7 @@ public static FastTreeRegressionTrainer FastTree(this RegressionCatalog.Regressi
public static FastTreeBinaryClassificationTrainer FastTree(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
- string weights = null,
+ string weightsColumn = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves,
@@ -75,7 +75,7 @@ public static FastTreeBinaryClassificationTrainer FastTree(this BinaryClassifica
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
- return new FastTreeBinaryClassificationTrainer(env, labelColumn, featureColumn, weights, numLeaves, numTrees, minDatapointsInLeaves, learningRate);
+ return new FastTreeBinaryClassificationTrainer(env, labelColumn, featureColumn, weightsColumn, numLeaves, numTrees, minDatapointsInLeaves, learningRate);
}
///
@@ -97,10 +97,10 @@ public static FastTreeBinaryClassificationTrainer FastTree(this BinaryClassifica
/// Ranks a series of inputs based on their relevance, training a decision tree ranking model through the .
///
/// The .
- /// The labelColumn column.
- /// The featureColumn column.
+ /// The name of the label column.
+ /// The name of the feature column.
/// The groupId column.
- /// The optional weights column.
+ /// The name of the optional weights column.
/// Total number of decision trees to create in the ensemble.
/// The maximum number of leaves per decision tree.
/// The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.
@@ -109,7 +109,7 @@ public static FastTreeRankingTrainer FastTree(this RankingCatalog.RankingTrainer
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string groupId = DefaultColumnNames.GroupId,
- string weights = null,
+ string weightsColumn = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves,
@@ -117,7 +117,7 @@ public static FastTreeRankingTrainer FastTree(this RankingCatalog.RankingTrainer
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
- return new FastTreeRankingTrainer(env, labelColumn, featureColumn, groupId, weights, numLeaves, numTrees, minDatapointsInLeaves, learningRate);
+ return new FastTreeRankingTrainer(env, labelColumn, featureColumn, groupId, weightsColumn, numLeaves, numTrees, minDatapointsInLeaves, learningRate);
}
///
@@ -139,23 +139,23 @@ public static FastTreeRankingTrainer FastTree(this RankingCatalog.RankingTrainer
/// Predict a target using generalized additive models trained with the .
///
/// The .
- /// The labelColumn column.
- /// The featureColumn column.
- /// The optional weights column.
+ /// The name of the label column.
+ /// The name of the feature column.
+ /// The name of the optional weights column.
/// The number of iterations to use in learning the features.
/// The learning rate. GAMs work best with a small learning rate.
/// The maximum number of bins to use to approximate features.
public static BinaryClassificationGamTrainer GeneralizedAdditiveModels(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
- string weights = null,
+ string weightsColumn = null,
int numIterations = GamDefaults.NumIterations,
double learningRate = GamDefaults.LearningRates,
int maxBins = GamDefaults.MaxBins)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
- return new BinaryClassificationGamTrainer(env, labelColumn, featureColumn, weights, numIterations, learningRate, maxBins);
+ return new BinaryClassificationGamTrainer(env, labelColumn, featureColumn, weightsColumn, numIterations, learningRate, maxBins);
}
///
@@ -175,23 +175,23 @@ public static BinaryClassificationGamTrainer GeneralizedAdditiveModels(this Bina
/// Predict a target using generalized additive models trained with the .
///
/// The .
- /// The labelColumn column.
- /// The featureColumn column.
- /// The optional weights column.
+ /// The name of the label column.
+ /// The name of the feature column.
+ /// The name of the optional weights column.
/// The number of iterations to use in learning the features.
/// The learning rate. GAMs work best with a small learning rate.
/// The maximum number of bins to use to approximate features.
public static RegressionGamTrainer GeneralizedAdditiveModels(this RegressionCatalog.RegressionTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
- string weights = null,
+ string weightsColumn = null,
int numIterations = GamDefaults.NumIterations,
double learningRate = GamDefaults.LearningRates,
int maxBins = GamDefaults.MaxBins)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
- return new RegressionGamTrainer(env, labelColumn, featureColumn, weights, numIterations, learningRate, maxBins);
+ return new RegressionGamTrainer(env, labelColumn, featureColumn, weightsColumn, numIterations, learningRate, maxBins);
}
///
@@ -211,9 +211,9 @@ public static RegressionGamTrainer GeneralizedAdditiveModels(this RegressionCata
/// Predict a target using a decision tree regression model trained with the .
///
/// The .
- /// The labelColumn column.
- /// The featureColumn column.
- /// The optional weights column.
+ /// The name of the label column.
+ /// The name of the feature column.
+ /// The name of the optional weights column.
/// Total number of decision trees to create in the ensemble.
/// The maximum number of leaves per decision tree.
/// The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.
@@ -221,7 +221,7 @@ public static RegressionGamTrainer GeneralizedAdditiveModels(this RegressionCata
public static FastTreeTweedieTrainer FastTreeTweedie(this RegressionCatalog.RegressionTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
- string weights = null,
+ string weightsColumn = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves,
@@ -229,7 +229,7 @@ public static FastTreeTweedieTrainer FastTreeTweedie(this RegressionCatalog.Regr
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
- return new FastTreeTweedieTrainer(env, labelColumn, featureColumn, weights, numLeaves, numTrees, minDatapointsInLeaves, learningRate);
+ return new FastTreeTweedieTrainer(env, labelColumn, featureColumn, weightsColumn, numLeaves, numTrees, minDatapointsInLeaves, learningRate);
}
///
@@ -251,9 +251,9 @@ public static FastTreeTweedieTrainer FastTreeTweedie(this RegressionCatalog.Regr
/// Predict a target using a decision tree regression model trained with the .
///
/// The .
- /// The labelColumn column.
- /// The featureColumn column.
- /// The optional weights column.
+ /// The name of the label column.
+ /// The name of the feature column.
+ /// The name of the optional weights column.
/// Total number of decision trees to create in the ensemble.
/// The maximum number of leaves per decision tree.
/// The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.
@@ -261,7 +261,7 @@ public static FastTreeTweedieTrainer FastTreeTweedie(this RegressionCatalog.Regr
public static FastForestRegression FastForest(this RegressionCatalog.RegressionTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
- string weights = null,
+ string weightsColumn = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves,
@@ -269,7 +269,7 @@ public static FastForestRegression FastForest(this RegressionCatalog.RegressionT
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
- return new FastForestRegression(env, labelColumn, featureColumn, weights, numLeaves, numTrees, minDatapointsInLeaves, learningRate);
+ return new FastForestRegression(env, labelColumn, featureColumn, weightsColumn, numLeaves, numTrees, minDatapointsInLeaves, learningRate);
}
///
@@ -291,9 +291,9 @@ public static FastForestRegression FastForest(this RegressionCatalog.RegressionT
/// Predict a target using a decision tree regression model trained with the .
///
/// The .
- /// The labelColumn column.
- /// The featureColumn column.
- /// The optional weights column.
+ /// The name of the label column.
+ /// The name of the feature column.
+ /// The name of the optional weights column.
/// Total number of decision trees to create in the ensemble.
/// The maximum number of leaves per decision tree.
/// The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.
@@ -301,7 +301,7 @@ public static FastForestRegression FastForest(this RegressionCatalog.RegressionT
public static FastForestClassification FastForest(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
- string weights = null,
+ string weightsColumn = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves,
@@ -309,7 +309,7 @@ public static FastForestClassification FastForest(this BinaryClassificationCatal
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
- return new FastForestClassification(env, labelColumn, featureColumn, weights,numLeaves, numTrees, minDatapointsInLeaves, learningRate);
+ return new FastForestClassification(env, labelColumn, featureColumn, weightsColumn,numLeaves, numTrees, minDatapointsInLeaves, learningRate);
}
///
diff --git a/src/Microsoft.ML.ImageAnalytics/ExtensionsCatalog.cs b/src/Microsoft.ML.ImageAnalytics/ExtensionsCatalog.cs
index b44afeb6a3..e93e441da1 100644
--- a/src/Microsoft.ML.ImageAnalytics/ExtensionsCatalog.cs
+++ b/src/Microsoft.ML.ImageAnalytics/ExtensionsCatalog.cs
@@ -89,8 +89,8 @@ public static ImagePixelExtractingEstimator ExtractPixels(this TransformsCatalog
///
///
/// The transform's catalog.
- /// Name of the input column.
- /// Name of the resulting output column.
+ /// Name of the column resulting from the transformation of .
+ /// Name of column to transform. If set to , the value of the will be used as source.
/// The transformed image width.
/// The transformed image height.
/// The type of image resizing as specified in .
diff --git a/src/Microsoft.ML.ImageAnalytics/ImagePixelExtractor.cs b/src/Microsoft.ML.ImageAnalytics/ImagePixelExtractor.cs
index 158e888310..d380fee318 100644
--- a/src/Microsoft.ML.ImageAnalytics/ImagePixelExtractor.cs
+++ b/src/Microsoft.ML.ImageAnalytics/ImagePixelExtractor.cs
@@ -699,7 +699,7 @@ internal void Save(ModelSaveContext ctx)
///
/// The host environment.
/// Name of the column resulting from the transformation of . Null means is replaced.
- /// Name of the input column.
+ /// Name of the column to transform. If set to , the value of the will be used as source.
/// What colors to extract.
/// Whether to interleave the pixels, meaning keep them in the `RGB RGB` order, or leave them in the plannar form: of all red pixels,
/// than all green, than all blue.
diff --git a/src/Microsoft.ML.ImageAnalytics/ImageResizer.cs b/src/Microsoft.ML.ImageAnalytics/ImageResizer.cs
index a48a80af4e..b612ea5a93 100644
--- a/src/Microsoft.ML.ImageAnalytics/ImageResizer.cs
+++ b/src/Microsoft.ML.ImageAnalytics/ImageResizer.cs
@@ -134,7 +134,7 @@ private static VersionInfo GetVersionInfo()
/// Name of the column resulting from the transformation of .
/// Width of resized image.
/// Height of resized image.
- /// Name of the input column.
+ /// Name of the column to transform. If set to , the value of the will be used as source.
/// What to use.
/// If set to what anchor to use for cropping.
internal ImageResizingTransformer(IHostEnvironment env, string outputColumnName,
@@ -523,7 +523,7 @@ public ColumnInfo(string name,
/// Name of the column resulting from the transformation of .
/// Width of resized image.
/// Height of resized image.
- /// Name of the input column.
+ /// The name of the input column.
/// What to use.
/// If set to what anchor to use for cropping.
internal ImageResizingEstimator(IHostEnvironment env,
diff --git a/src/Microsoft.ML.LightGBM/LightGbmCatalog.cs b/src/Microsoft.ML.LightGBM/LightGbmCatalog.cs
index 08a968ec5d..d540cf3646 100644
--- a/src/Microsoft.ML.LightGBM/LightGbmCatalog.cs
+++ b/src/Microsoft.ML.LightGBM/LightGbmCatalog.cs
@@ -17,9 +17,9 @@ public static class LightGbmExtensions
/// Predict a target using a decision tree regression model trained with the .
///
/// The .
- /// The labelColumn column.
- /// The features column.
- /// The weights column.
+ /// The name of the label column.
+ /// The name of the features column.
+ /// The name of the optional weights column.
/// The number of leaves to use.
/// Number of iterations.
/// The minimal number of documents allowed in a leaf of the tree, out of the subsampled data.
@@ -69,9 +69,9 @@ public static LightGbmRegressorTrainer LightGbm(this RegressionCatalog.Regressio
/// Predict a target using a decision tree binary classification model trained with the .
///
/// The .
- /// The labelColumn column.
- /// The features column.
- /// The weights column.
+ /// The name of the label column.
+ /// The name of the features column.
+ /// The name of the optional weights column.
/// The number of leaves to use.
/// Number of iterations.
/// The minimal number of documents allowed in a leaf of the tree, out of the subsampled data.
@@ -121,9 +121,9 @@ public static LightGbmBinaryTrainer LightGbm(this BinaryClassificationCatalog.Bi
/// Predict a target using a decision tree ranking model trained with the .
///
/// The .
- /// The labelColumn column.
- /// The features column.
- /// The weights column.
+ /// The name of the label column.
+ /// The name of the features column.
+ /// The name of the optional weights column.
/// The groupId column.
/// The number of leaves to use.
/// Number of iterations.
@@ -161,9 +161,9 @@ public static LightGbmRankingTrainer LightGbm(this RankingCatalog.RankingTrainer
/// Predict a target using a decision tree multiclass classification model trained with the .
///
/// The .
- /// The labelColumn column.
- /// The features column.
- /// The weights column.
+ /// The name of the label column.
+ /// The name of the features column.
+ /// The name of the optional weights column.
/// The number of leaves to use.
/// Number of iterations.
/// The minimal number of documents allowed in a leaf of the tree, out of the subsampled data.
diff --git a/src/Microsoft.ML.LightGBM/LightGbmMulticlassTrainer.cs b/src/Microsoft.ML.LightGBM/LightGbmMulticlassTrainer.cs
index 6fb7cb70e3..4019fabe00 100644
--- a/src/Microsoft.ML.LightGBM/LightGbmMulticlassTrainer.cs
+++ b/src/Microsoft.ML.LightGBM/LightGbmMulticlassTrainer.cs
@@ -44,9 +44,9 @@ internal LightGbmMulticlassTrainer(IHostEnvironment env, Options options)
/// Initializes a new instance of
///
/// The private instance of .
- /// The name of The label column.
+ /// The name of the label column.
/// The name of the feature column.
- /// The name for the column containing the initial weight.
+ /// The name of the optional weights column.
/// The number of leaves to use.
/// Number of iterations.
/// The minimal number of documents allowed in a leaf of the tree, out of the subsampled data.
diff --git a/src/Microsoft.ML.StandardLearners/Standard/Online/OnlineGradientDescent.cs b/src/Microsoft.ML.StandardLearners/Standard/Online/OnlineGradientDescent.cs
index 572e4bfce9..5292eed5bd 100644
--- a/src/Microsoft.ML.StandardLearners/Standard/Online/OnlineGradientDescent.cs
+++ b/src/Microsoft.ML.StandardLearners/Standard/Online/OnlineGradientDescent.cs
@@ -91,8 +91,8 @@ public override LinearRegressionModelParameters CreatePredictor()
/// Trains a new .
///
/// The pricate instance of .
- /// Name of the label column.
- /// Name of the feature column.
+ /// The name of the label column.
+ /// The name of the feature column.
/// The learning Rate.
/// Decrease learning rate as iterations progress.
/// L2 Regularization Weight.
diff --git a/src/Microsoft.ML.StaticPipe/TransformsStatic.cs b/src/Microsoft.ML.StaticPipe/TransformsStatic.cs
index 4e42fd722c..3edb7d5b18 100644
--- a/src/Microsoft.ML.StaticPipe/TransformsStatic.cs
+++ b/src/Microsoft.ML.StaticPipe/TransformsStatic.cs
@@ -126,8 +126,8 @@ public override IEstimator Reconcile(IHostEnvironment env,
}
///
- /// Name of the input column.
- /// Name of the column to use for labels.
+ /// The name of the input column.
+ /// The name of the column to use for labels.
/// The maximum number of slots to preserve in the output. The number of slots to preserve is taken across all input columns.
/// Max number of bins used to approximate mutual information between each input column and the label column. Power of 2 recommended.
///
@@ -144,8 +144,8 @@ public static Vector SelectFeaturesBasedOnMutualInformation(
int numBins = MutualInformationFeatureSelectingEstimator.Defaults.NumBins) => new OutPipelineColumn(input, labelColumn, slotsInOutput, numBins);
///
- /// Name of the input column.
- /// Name of the column to use for labels.
+ /// The name of the input column.
+ /// The name of the column to use for labels.
/// The maximum number of slots to preserve in the output. The number of slots to preserve is taken across all input columns.
/// Max number of bins used to approximate mutual information between each input column and the label column. Power of 2 recommended.
///