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()
         /// </summary>
         /// <param name="env">Host Environment.</param>
         /// <param name="input">Input <see cref="IDataView"/>. This is the output from previous transform or loader.</param>
-        /// <param name="name">Name of the output column.</param>
+        /// <param name="name">The name of the output column.</param>
         /// <param name="seed">Seed to start random number generator.</param>
         /// <param name="useCounter">Use an auto-incremented integer starting at zero instead of a random number.</param>
         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()
         /// </summary>
         /// <param name="env">Host Environment.</param>
         /// <param name="input">Input <see cref="IDataView"/>. This is the output from previous transform or loader.</param>
-        /// <param name="outputColumnName">Name of the output column.</param>
-        /// <param name="inputColumnName">Name of the input column.  If this is null '<paramref name="outputColumnName"/>' will be used.</param>
+        /// <param name="outputColumnName">The name of the output column.</param>
+        /// <param name="inputColumnName">The name of the input column.  If this is null '<paramref name="outputColumnName"/>' will be used.</param>
         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)
         /// <param name="env">Host Environment.</param>
         /// <param name="input">Input <see cref="IDataView"/>. This is the output from previous transform or loader.</param>
         /// <param name="classIndex">Label of the positive class.</param>
-        /// <param name="name">Name of the output column.</param>
-        /// <param name="source">Name of the input column.  If this is null '<paramref name="name"/>' will be used.</param>
+        /// <param name="name">The name of the output column.</param>
+        /// <param name="source">The name of the input column.  If this is null '<paramref name="name"/>' will be used.</param>
         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
         /// </summary>
         /// <param name="env">Host Environment.</param>
         /// <param name="input">Input <see cref="IDataView"/>. This is the output from previous transform or loader.</param>
-        /// <param name="outputColumnName">Name of the output column.</param>
-        /// <param name="inputColumnName">Name of the column to be transformed. If this is null '<paramref name="outputColumnName"/>' will be used.</param>
+        /// <param name="outputColumnName">The name of the output column.</param>
+        /// <param name="inputColumnName">The name of the column to be transformed. If this is null '<paramref name="outputColumnName"/>' will be used.</param>
         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()
         /// </summary>
         /// <param name="env">Host Environment.</param>
         /// <param name="input">Input <see cref="IDataView"/>. This is the output from previous transform or loader.</param>
-        /// <param name="column">Name of the input column.</param>
+        /// <param name="column">The name of the input column.</param>
         /// <param name="lowerBound">Minimum value (0 to 1 for key types).</param>
         /// <param name="upperBound">Maximum value (0 to 1 for key types).</param>
         /// <param name="includeUpperBound">Whether to include the upper bound.</param>
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.
         /// </summary>
         /// <param name="env">Host Environment.</param>
-        /// <param name="outputColumnName">Name of the output column.</param>
-        /// <param name="inputColumnName">Name of the column to be transformed. If this is null '<paramref name="outputColumnName"/>' will be used.</param>
+        /// <param name="outputColumnName">The name of the output column.</param>
+        /// <param name="inputColumnName">The name of the column to be transformed. If this is null '<paramref name="outputColumnName"/>' will be used.</param>
         /// <param name="outputKind">The expected type of the converted column.</param>
         /// <param name="outputKeyCount">New key count if we work with key type.</param>
         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 <see cref="FastTreeRegressionTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
-        /// <param name="labelColumn">The label column.</param>
-        /// <param name="featureColumn">The feature column.</param>
-        /// <param name="weights">The optional weights column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the feature column.</param>
+        /// <param name="weightsColumn">The name of the optional weights column.</param>
         /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
         /// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
         /// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of a regression tree, out of the subsampled data.</param>
@@ -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);
         }
 
         /// <summary>
@@ -57,9 +57,9 @@ public static FastTreeRegressionTrainer FastTree(this RegressionCatalog.Regressi
         /// Predict a target using a decision tree binary classification model trained with the <see cref="FastTreeBinaryClassificationTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
-        /// <param name="featureColumn">The featureColumn column.</param>
-        /// <param name="weights">The optional weights column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the feature column.</param>
+        /// <param name="weightsColumn">The name of the optional weights column.</param>
         /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
         /// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
         /// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
@@ -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);
         }
 
         /// <summary>
@@ -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 <see cref="FastTreeRankingTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="RankingCatalog"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
-        /// <param name="featureColumn">The featureColumn column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the feature column.</param>
         /// <param name="groupId">The groupId column.</param>
-        /// <param name="weights">The optional weights column.</param>
+        /// <param name="weightsColumn">The name of the optional weights column.</param>
         /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
         /// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
         /// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
@@ -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);
         }
 
         /// <summary>
@@ -139,23 +139,23 @@ public static FastTreeRankingTrainer FastTree(this RankingCatalog.RankingTrainer
         /// Predict a target using generalized additive models trained with the <see cref="BinaryClassificationGamTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
-        /// <param name="featureColumn">The featureColumn column.</param>
-        /// <param name="weights">The optional weights column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the feature column.</param>
+        /// <param name="weightsColumn">The name of the optional weights column.</param>
         /// <param name="numIterations">The number of iterations to use in learning the features.</param>
         /// <param name="learningRate">The learning rate. GAMs work best with a small learning rate.</param>
         /// <param name="maxBins">The maximum number of bins to use to approximate features.</param>
         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);
         }
 
         /// <summary>
@@ -175,23 +175,23 @@ public static BinaryClassificationGamTrainer GeneralizedAdditiveModels(this Bina
         /// Predict a target using generalized additive models trained with the <see cref="RegressionGamTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
-        /// <param name="featureColumn">The featureColumn column.</param>
-        /// <param name="weights">The optional weights column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the feature column.</param>
+        /// <param name="weightsColumn">The name of the optional weights column.</param>
         /// <param name="numIterations">The number of iterations to use in learning the features.</param>
         /// <param name="learningRate">The learning rate. GAMs work best with a small learning rate.</param>
         /// <param name="maxBins">The maximum number of bins to use to approximate features.</param>
         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);
         }
 
         /// <summary>
@@ -211,9 +211,9 @@ public static RegressionGamTrainer GeneralizedAdditiveModels(this RegressionCata
         /// Predict a target using a decision tree regression model trained with the <see cref="FastTreeTweedieTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
-        /// <param name="featureColumn">The featureColumn column.</param>
-        /// <param name="weights">The optional weights column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the feature column.</param>
+        /// <param name="weightsColumn">The name of the optional weights column.</param>
         /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
         /// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
         /// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
@@ -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);
         }
 
         /// <summary>
@@ -251,9 +251,9 @@ public static FastTreeTweedieTrainer FastTreeTweedie(this RegressionCatalog.Regr
         /// Predict a target using a decision tree regression model trained with the <see cref="FastForestRegression"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
-        /// <param name="featureColumn">The featureColumn column.</param>
-        /// <param name="weights">The optional weights column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the feature column.</param>
+        /// <param name="weightsColumn">The name of the optional weights column.</param>
         /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
         /// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
         /// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
@@ -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);
         }
 
         /// <summary>
@@ -291,9 +291,9 @@ public static FastForestRegression FastForest(this RegressionCatalog.RegressionT
         /// Predict a target using a decision tree regression model trained with the <see cref="FastForestClassification"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
-        /// <param name="featureColumn">The featureColumn column.</param>
-        /// <param name="weights">The optional weights column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the feature column.</param>
+        /// <param name="weightsColumn">The name of the optional weights column.</param>
         /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
         /// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
         /// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
@@ -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);
         }
 
         /// <summary>
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
         /// <seealso cref= "ImageLoadingEstimator" />
         /// </remarks >
         /// <param name="catalog">The transform's catalog.</param>
-        /// <param name="inputColumnName">Name of the input column.</param>
-        /// <param name="outputColumnName">Name of the resulting output column.</param>
+        /// <param name="inputColumnName">Name of the column resulting from the transformation of <paramref name="outputColumnName"/>.</param>
+        /// <param name="outputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="inputColumnName"/> will be used as source.</param>
         /// <param name="imageWidth">The transformed image width.</param>
         /// <param name="imageHeight">The transformed image height.</param>
         /// <param name="resizing"> The type of image resizing as specified in <see cref="ImageResizingEstimator.ResizingKind"/>.</param>
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)
         ///</summary>
         /// <param name="env">The host environment.</param>
         /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>. Null means <paramref name="inputColumnName"/> is replaced.</param>
-        /// <param name="inputColumnName">Name of the input column.</param>
+        /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
         /// <param name="colors">What colors to extract.</param>
         /// <param name="interleave">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.</param>
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()
         /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
         /// <param name="imageWidth">Width of resized image.</param>
         /// <param name="imageHeight">Height of resized image.</param>
-        /// <param name="inputColumnName">Name of the input column.</param>
+        /// <param name="inputColumnName">Name of the column to transform. If set to , the value of the will be used as source.</param>
         /// <param name="resizing">What <see cref="ImageResizingEstimator.ResizingKind"/> to use.</param>
         /// <param name="cropAnchor">If <paramref name="resizing"/> set to <see cref="ImageResizingEstimator.ResizingKind.IsoCrop"/> what anchor to use for cropping.</param>
         internal ImageResizingTransformer(IHostEnvironment env, string outputColumnName,
@@ -523,7 +523,7 @@ public ColumnInfo(string name,
         /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
         /// <param name="imageWidth">Width of resized image.</param>
         /// <param name="imageHeight">Height of resized image.</param>
-        /// <param name="inputColumnName">Name of the input column.</param>
+        /// <param name="inputColumnName">The name of the input column.</param>
         /// <param name="resizing">What <see cref="ResizingKind"/> to use.</param>
         /// <param name="cropAnchor">If <paramref name="resizing"/> set to <see cref="ResizingKind.IsoCrop"/> what anchor to use for cropping.</param>
         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 <see cref="LightGbmRegressorTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
-        /// <param name="featureColumn">The features column.</param>
-        /// <param name="weights">The weights column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the features column.</param>
+        /// <param name="weights">The name of the optional weights column.</param>
         /// <param name="numLeaves">The number of leaves to use.</param>
         /// <param name="numBoostRound">Number of iterations.</param>
         /// <param name="minDataPerLeaf">The minimal number of documents allowed in a leaf of the tree, out of the subsampled data.</param>
@@ -69,9 +69,9 @@ public static LightGbmRegressorTrainer LightGbm(this RegressionCatalog.Regressio
         /// Predict a target using a decision tree binary classification model trained with the <see cref="LightGbmBinaryTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
-        /// <param name="featureColumn">The features column.</param>
-        /// <param name="weights">The weights column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the features column.</param>
+        /// <param name="weights">The name of the optional weights column.</param>
         /// <param name="numLeaves">The number of leaves to use.</param>
         /// <param name="numBoostRound">Number of iterations.</param>
         /// <param name="minDataPerLeaf">The minimal number of documents allowed in a leaf of the tree, out of the subsampled data.</param>
@@ -121,9 +121,9 @@ public static LightGbmBinaryTrainer LightGbm(this BinaryClassificationCatalog.Bi
         /// Predict a target using a decision tree ranking model trained with the <see cref="LightGbmRankingTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="RankingCatalog"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
-        /// <param name="featureColumn">The features column.</param>
-        /// <param name="weights">The weights column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the features column.</param>
+        /// <param name="weights">The name of the optional weights column.</param>
         /// <param name="groupIdColumn">The groupId column.</param>
         /// <param name="numLeaves">The number of leaves to use.</param>
         /// <param name="numBoostRound">Number of iterations.</param>
@@ -161,9 +161,9 @@ public static LightGbmRankingTrainer LightGbm(this RankingCatalog.RankingTrainer
         /// Predict a target using a decision tree multiclass classification model trained with the <see cref="LightGbmMulticlassTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="MulticlassClassificationCatalog"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
-        /// <param name="featureColumn">The features column.</param>
-        /// <param name="weights">The weights column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the features column.</param>
+        /// <param name="weights">The name of the optional weights column.</param>
         /// <param name="numLeaves">The number of leaves to use.</param>
         /// <param name="numBoostRound">Number of iterations.</param>
         /// <param name="minDataPerLeaf">The minimal number of documents allowed in a leaf of the tree, out of the subsampled data.</param>
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 <see cref="LightGbmMulticlassTrainer"/>
         /// </summary>
         /// <param name="env">The private instance of <see cref="IHostEnvironment"/>.</param>
-        /// <param name="labelColumn">The name of The label column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
         /// <param name="featureColumn">The name of the feature column.</param>
-        /// <param name="weights">The name for the column containing the initial weight.</param>
+        /// <param name="weights">The name of the optional weights column.</param>
         /// <param name="numLeaves">The number of leaves to use.</param>
         /// <param name="numBoostRound">Number of iterations.</param>
         /// <param name="minDataPerLeaf">The minimal number of documents allowed in a leaf of the tree, out of the subsampled data.</param>
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 <see cref="RegressionPredictionTransformer{LinearRegressionPredictor}"/>.
         /// </summary>
         /// <param name="env">The pricate instance of <see cref="IHostEnvironment"/>.</param>
-        /// <param name="labelColumn">Name of the label column.</param>
-        /// <param name="featureColumn">Name of the feature column.</param>
+        /// <param name="labelColumn">The name of the label column.</param>
+        /// <param name="featureColumn">The name of the feature column.</param>
         /// <param name="learningRate">The learning Rate.</param>
         /// <param name="decreaseLearningRate">Decrease learning rate as iterations progress.</param>
         /// <param name="l2RegularizerWeight">L2 Regularization Weight.</param>
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<ITransformer> Reconcile(IHostEnvironment env,
         }
 
         /// <include file='../Microsoft.ML.Transforms/doc.xml' path='doc/members/member[@name="MutualInformationFeatureSelection"]/*' />
-        /// <param name="input">Name of the input column.</param>
-        /// <param name="labelColumn">Name of the column to use for labels.</param>
+        /// <param name="input">The name of the input column.</param>
+        /// <param name="labelColumn">The name of the column to use for labels.</param>
         /// <param name="slotsInOutput">The maximum number of slots to preserve in the output. The number of slots to preserve is taken across all input columns.</param>
         /// <param name="numBins">Max number of bins used to approximate mutual information between each input column and the label column. Power of 2 recommended.</param>
         /// <example>
@@ -144,8 +144,8 @@ public static Vector<float> SelectFeaturesBasedOnMutualInformation(
             int numBins = MutualInformationFeatureSelectingEstimator.Defaults.NumBins) => new OutPipelineColumn<float>(input, labelColumn, slotsInOutput, numBins);
 
         /// <include file='../Microsoft.ML.Transforms/doc.xml' path='doc/members/member[@name="MutualInformationFeatureSelection"]/*' />
-        /// <param name="input">Name of the input column.</param>
-        /// <param name="labelColumn">Name of the column to use for labels.</param>
+        /// <param name="input">The name of the input column.</param>
+        /// <param name="labelColumn">The name of the column to use for labels.</param>
         /// <param name="slotsInOutput">The maximum number of slots to preserve in the output. The number of slots to preserve is taken across all input columns.</param>
         /// <param name="numBins">Max number of bins used to approximate mutual information between each input column and the label column. Power of 2 recommended.</param>
         /// <example>