Changed parameters description as per the comment of @Ivanidzo4ka

src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs

@@ -19,23 +19,23 @@ public static class TreeExtensions
         /// <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="weightsColumn">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>
         /// <param name="learningRate">The learning rate.</param>
         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,
             double learningRate = Defaults.LearningRates)
         {
             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>
@@ -59,23 +59,23 @@ public static FastTreeRegressionTrainer FastTree(this RegressionCatalog.Regressi
         /// <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="weightsColumn">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>
         /// <param name="learningRate">The learning rate.</param>
         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,
             double learningRate = Defaults.LearningRates)
         {
             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>
@@ -100,7 +100,7 @@ public static FastTreeBinaryClassificationTrainer FastTree(this BinaryClassifica
         /// <param name="labelColumn">The labelColumn column.</param>
         /// <param name="featureColumn">The featureColumn column.</param>
         /// <param name="groupId">The groupId column.</param>
-        /// <param name="weights">The optional weights column.</param>
+        /// <param name="weightsColumn">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>
@@ -109,15 +109,15 @@ 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,
             double learningRate = Defaults.LearningRates)
         {
             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>
@@ -141,21 +141,21 @@ public static FastTreeRankingTrainer FastTree(this RankingCatalog.RankingTrainer
         /// <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="weightsColumn">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>
@@ -177,21 +177,21 @@ public static BinaryClassificationGamTrainer GeneralizedAdditiveModels(this Bina
         /// <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="weightsColumn">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>
@@ -213,23 +213,23 @@ public static RegressionGamTrainer GeneralizedAdditiveModels(this RegressionCata
         /// <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="weightsColumn">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>
         /// <param name="learningRate">The learning rate.</param>
         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,
             double learningRate = Defaults.LearningRates)
         {
             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>
@@ -253,23 +253,23 @@ public static FastTreeTweedieTrainer FastTreeTweedie(this RegressionCatalog.Regr
         /// <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="weightsColumn">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>
         /// <param name="learningRate">The learning rate.</param>
         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,
             double learningRate = Defaults.LearningRates)
         {
             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>
@@ -293,23 +293,23 @@ public static FastForestRegression FastForest(this RegressionCatalog.RegressionT
         /// <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="weightsColumn">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>
         /// <param name="learningRate">The learning rate.</param>
         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,
             double learningRate = Defaults.LearningRates)
         {
             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>

src/Microsoft.ML.HalLearners/HalLearnersCatalog.cs

@@ -22,19 +22,19 @@ public static class HalLearnersCatalog
         /// <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="weightsColumn">The optional weights column.</param>
         public static OlsLinearRegressionTrainer OrdinaryLeastSquares(this RegressionCatalog.RegressionTrainers catalog,
             string labelColumn = DefaultColumnNames.Label,
             string featureColumn = DefaultColumnNames.Features,
-            string weights = null)
+            string weightsColumn = null)
         {
             Contracts.CheckValue(catalog, nameof(catalog));
             var env = CatalogUtils.GetEnvironment(catalog);
             var options = new OlsLinearRegressionTrainer.Options
             {
                 LabelColumn = labelColumn,
                 FeatureColumn = featureColumn,
-                WeightColumn = weights != null ? Optional<string>.Explicit(weights) : Optional<string>.Implicit(DefaultColumnNames.Weight)
+                WeightColumn = weightsColumn != null ? Optional<string>.Explicit(weightsColumn) : Optional<string>.Implicit(DefaultColumnNames.Weight)
             };
 
             return new OlsLinearRegressionTrainer(env, options);

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="inputColumnName"/>.</param>
+        /// <param name="outputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> 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>

src/Microsoft.ML.ImageAnalytics/ImagePixelExtractor.cs

@@ -700,7 +700,7 @@ 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>

src/Microsoft.ML.ImageAnalytics/ImageResizer.cs

@@ -135,7 +135,7 @@ 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,

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>