Rename FeatureContributionClaculator => FeatureContributionCalculator

src/Microsoft.ML.Data/Dirty/PredictorInterfaces.cs

@@ -208,7 +208,7 @@ internal interface IFeatureContributionMapper : IPredictor
     /// </summary>
     public interface ICalculateFeatureContribution : IPredictor
     {
-        FeatureContributionCalculator FeatureContributionClaculator { get; }
+        FeatureContributionCalculator FeatureContributionCalculator { get; }
     }
 
     /// <summary>

src/Microsoft.ML.Data/Prediction/Calibrator.cs

@@ -216,7 +216,7 @@ public abstract class ValueMapperCalibratedPredictorBase : CalibratedPredictorBa
         ColumnType IValueMapperDist.DistType => NumberType.Float;
         bool ICanSavePfa.CanSavePfa => (_mapper as ICanSavePfa)?.CanSavePfa == true;
 
-        public FeatureContributionCalculator FeatureContributionClaculator => new FeatureContributionCalculator(this);
+        public FeatureContributionCalculator FeatureContributionCalculator => new FeatureContributionCalculator(this);
 
         bool ICanSaveOnnx.CanSaveOnnx(OnnxContext ctx) => (_mapper as ICanSaveOnnx)?.CanSaveOnnx(ctx) == true;
 

src/Microsoft.ML.FastTree/FastTree.cs

@@ -2845,7 +2845,7 @@ public abstract class TreeEnsembleModelParameters :
         /// and the score obtained by taking the opposite decision at the node corresponding to feature F1. This algorithm extends naturally to models with
         /// many decision trees.
         /// </summary>
-        public FeatureContributionCalculator FeatureContributionClaculator => new FeatureContributionCalculator(this);
+        public FeatureContributionCalculator FeatureContributionCalculator => new FeatureContributionCalculator(this);
 
         public TreeEnsembleModelParameters(IHostEnvironment env, string name, TreeEnsemble trainedEnsemble, int numFeatures, string innerArgs)
             : base(env, name)

src/Microsoft.ML.FastTree/GamTrainer.cs

@@ -673,7 +673,7 @@ public abstract class GamModelParametersBase : ModelParametersBase<float>, IValu
         /// For Generalized Additive Models (GAM), the contribution of a feature is equal to the shape function for the given feature evaluated at
         /// the feature value.
         /// </summary>
-        public FeatureContributionCalculator FeatureContributionClaculator => new FeatureContributionCalculator(this);
+        public FeatureContributionCalculator FeatureContributionCalculator => new FeatureContributionCalculator(this);
 
         private protected GamModelParametersBase(IHostEnvironment env, string name,
             int inputLength, Dataset trainSet, double meanEffect, double[][] binEffects, int[] featureMap)

src/Microsoft.ML.StandardLearners/Standard/LinearModelParameters.cs

@@ -103,7 +103,7 @@ public IEnumerator<float> GetEnumerator()
         /// Used to determine the contribution of each feature to the score of an example by <see cref="FeatureContributionCalculatingTransformer"/>.
         /// For linear models, the contribution of a given feature is equal to the product of feature value times the corresponding weight.
         /// </summary>
-        public FeatureContributionCalculator FeatureContributionClaculator => new FeatureContributionCalculator(this);
+        public FeatureContributionCalculator FeatureContributionCalculator => new FeatureContributionCalculator(this);
 
         /// <summary>
         /// Constructs a new linear predictor.