Update core_ep-list.tsv

Author: najeeb-kazmi

src/Microsoft.ML.FastTree/Properties/AssemblyInfo.cs

@@ -6,6 +6,6 @@
 using Microsoft.ML;
 
 [assembly: InternalsVisibleTo(assemblyName: "Microsoft.ML.Core.Tests" + PublicKey.TestValue)]
-[assembly: InternalsVisibleTo(assemblyName: "Microsoft.ML.LightGBM" + PublicKey.TestValue)]
+[assembly: InternalsVisibleTo(assemblyName: "Microsoft.ML.LightGBM" + PublicKey.Value)]
 
 [assembly: WantsToBeBestFriends]

test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv

@@ -57,7 +57,7 @@ Trainers.FastTreeTweedieRegressor	Trains gradient boosted decision trees to fit
 Trainers.FieldAwareFactorizationMachineBinaryClassifier	Train a field-aware factorization machine for binary classification	Microsoft.ML.Runtime.FactorizationMachine.FieldAwareFactorizationMachineTrainer	TrainBinary	Microsoft.ML.Runtime.FactorizationMachine.FieldAwareFactorizationMachineTrainer+Arguments	Microsoft.ML.Runtime.EntryPoints.CommonOutputs+BinaryClassificationOutput
 Trainers.GeneralizedAdditiveModelBinaryClassifier	Trains a gradient boosted stump per feature, on all features simultaneously, to fit target values using least-squares. It mantains no interactions between features.	Microsoft.ML.Trainers.FastTree.Gam	TrainBinary	Microsoft.ML.Trainers.FastTree.BinaryClassificationGamTrainer+Arguments	Microsoft.ML.Runtime.EntryPoints.CommonOutputs+BinaryClassificationOutput
 Trainers.GeneralizedAdditiveModelRegressor	Trains a gradient boosted stump per feature, on all features simultaneously, to fit target values using least-squares. It mantains no interactions between features.	Microsoft.ML.Trainers.FastTree.Gam	TrainRegression	Microsoft.ML.Trainers.FastTree.RegressionGamTrainer+Arguments	Microsoft.ML.Runtime.EntryPoints.CommonOutputs+RegressionOutput
-Trainers.KMeansPlusPlusClusterer	K-means is a popular clustering algorithm. With K-means, the data is clustered into a specified number of clusters in order to minimize the within-cluster sum of squares. K-means++ improves upon K-means by using a better method for choosing the initial cluster centers.	Microsoft.ML.Trainers.KMeans.KMeansPlusPlusTrainer	TrainKMeans	Microsoft.ML.Trainers.KMeans.KMeansPlusPlusTrainer+Arguments	Microsoft.ML.Runtime.EntryPoints.CommonOutputs+ClusteringOutput
+Trainers.KMeansPlusPlusClusterer	K-means is a popular clustering algorithm. With K-means, the data is clustered into a specified number of clusters in order to minimize the within-cluster sum of squares. K-means++ improves upon K-means by using a better method for choosing the initial cluster centers.	Microsoft.ML.KMeansClustering.KMeansPlusPlusTrainer	TrainKMeans	Microsoft.ML.KMeansClustering.KMeansPlusPlusTrainer+Arguments	Microsoft.ML.Runtime.EntryPoints.CommonOutputs+ClusteringOutput
 Trainers.LightGbmBinaryClassifier	Train a LightGBM binary classification model.	Microsoft.ML.Runtime.LightGBM.LightGbm	TrainBinary	Microsoft.ML.Runtime.LightGBM.LightGbmArguments	Microsoft.ML.Runtime.EntryPoints.CommonOutputs+BinaryClassificationOutput
 Trainers.LightGbmClassifier	Train a LightGBM multi class model.	Microsoft.ML.Runtime.LightGBM.LightGbm	TrainMultiClass	Microsoft.ML.Runtime.LightGBM.LightGbmArguments	Microsoft.ML.Runtime.EntryPoints.CommonOutputs+MulticlassClassificationOutput
 Trainers.LightGbmRanker	Train a LightGBM ranking model.	Microsoft.ML.Runtime.LightGBM.LightGbm	TrainRanking	Microsoft.ML.Runtime.LightGBM.LightGbmArguments	Microsoft.ML.Runtime.EntryPoints.CommonOutputs+RankingOutput