fixed pipeline

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

@@ -51,8 +51,8 @@ Trainers.FastTreeRanker	Trains gradient boosted decision trees to the LambdaRank
 Trainers.FastTreeRegressor	Trains gradient boosted decision trees to fit target values using least-squares.	Microsoft.ML.Trainers.FastTree.FastTree	TrainRegression	Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
 Trainers.FastTreeTweedieRegressor	Trains gradient boosted decision trees to fit target values using a Tweedie loss function. This learner is a generalization of Poisson, compound Poisson, and gamma regression.	Microsoft.ML.Trainers.FastTree.FastTree	TrainTweedieRegression	Microsoft.ML.Trainers.FastTree.FastTreeTweedieTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
 Trainers.FieldAwareFactorizationMachineBinaryClassifier	Train a field-aware factorization machine for binary classification	Microsoft.ML.Trainers.FieldAwareFactorizationMachineTrainer	TrainBinary	Microsoft.ML.Trainers.FieldAwareFactorizationMachineTrainer+Options	Microsoft.ML.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.GamBinaryTrainer+Options	Microsoft.ML.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.GamRegressionTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
+Trainers.GeneralizedAdditiveModelBinaryClassifier	Trains a gradient boosted stump per feature, on all features simultaneously, to fit target values using least-squares. It maintains no interactions between features.	Microsoft.ML.Trainers.FastTree.Gam	TrainBinary	Microsoft.ML.Trainers.FastTree.GamBinaryTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
+Trainers.GeneralizedAdditiveModelRegressor	Trains a gradient boosted stump per feature, on all features simultaneously, to fit target values using least-squares. It maintains no interactions between features.	Microsoft.ML.Trainers.FastTree.Gam	TrainRegression	Microsoft.ML.Trainers.FastTree.GamRegressionTrainer+Options	Microsoft.ML.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.KMeansTrainer	TrainKMeans	Microsoft.ML.Trainers.KMeansTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+ClusteringOutput
 Trainers.LightGbmBinaryClassifier	Train a LightGBM binary classification model.	Microsoft.ML.Trainers.LightGbm.LightGbm	TrainBinary	Microsoft.ML.Trainers.LightGbm.LightGbmBinaryTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
 Trainers.LightGbmClassifier	Train a LightGBM multi class model.	Microsoft.ML.Trainers.LightGbm.LightGbm	TrainMulticlass	Microsoft.ML.Trainers.LightGbm.LightGbmMulticlassTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput
@@ -107,7 +107,7 @@ Transforms.LpNormalizer	Normalize vectors (rows) individually by rescaling them
 Transforms.ManyHeterogeneousModelCombiner	Combines a sequence of TransformModels and a PredictorModel into a single PredictorModel.	Microsoft.ML.EntryPoints.ModelOperations	CombineModels	Microsoft.ML.EntryPoints.ModelOperations+PredictorModelInput	Microsoft.ML.EntryPoints.ModelOperations+PredictorModelOutput
 Transforms.MeanVarianceNormalizer	Normalizes the data based on the computed mean and variance of the data.	Microsoft.ML.Data.Normalize	MeanVar	Microsoft.ML.Transforms.NormalizeTransform+MeanVarArguments	Microsoft.ML.EntryPoints.CommonOutputs+TransformOutput
 Transforms.MinMaxNormalizer	Normalizes the data based on the observed minimum and maximum values of the data.	Microsoft.ML.Data.Normalize	MinMax	Microsoft.ML.Transforms.NormalizeTransform+MinMaxArguments	Microsoft.ML.EntryPoints.CommonOutputs+TransformOutput
-Transforms.MissingValueHandler	Handle missing values by replacing them with either the default value or the mean/min/max value (for non-text columns only). An indicator column can optionally be concatenated, if theinput column type is numeric.	Microsoft.ML.Transforms.NAHandling	Handle	Microsoft.ML.Transforms.MissingValueHandlingTransformer+Options	Microsoft.ML.EntryPoints.CommonOutputs+TransformOutput
+Transforms.MissingValueHandler	Handle missing values by replacing them with either the default value or the mean/min/max value (for non-text columns only). An indicator column can optionally be concatenated, if the input column type is numeric.	Microsoft.ML.Transforms.NAHandling	Handle	Microsoft.ML.Transforms.MissingValueHandlingTransformer+Options	Microsoft.ML.EntryPoints.CommonOutputs+TransformOutput
 Transforms.MissingValueIndicator	Create a boolean output column with the same number of slots as the input column, where the output value is true if the value in the input column is missing.	Microsoft.ML.Transforms.NAHandling	Indicator	Microsoft.ML.Transforms.MissingValueIndicatorTransformer+Options	Microsoft.ML.EntryPoints.CommonOutputs+TransformOutput
 Transforms.MissingValuesDropper	Removes NAs from vector columns.	Microsoft.ML.Transforms.NAHandling	Drop	Microsoft.ML.Transforms.MissingValueDroppingTransformer+Options	Microsoft.ML.EntryPoints.CommonOutputs+TransformOutput
 Transforms.MissingValuesRowDropper	Filters out rows that contain missing values.	Microsoft.ML.Transforms.NAHandling	Filter	Microsoft.ML.Transforms.NAFilter+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+TransformOutput