Microsoft.ML.Transforms Namespace
Namespace containing data transformation components.
Classes
ApproximatedKernelMappingEstimator 
Maps vector columns to a low dimensional feature space. 
ApproximatedKernelTransformer 
ITransformer resulting from fitting an ApproximatedKernelMappingEstimator. 
ColumnConcatenatingEstimator 
Concatenates one or more input columns into a new output column. 
ColumnCopyingEstimator  
ColumnCopyingTransformer 
ITransformer resulting from fitting a ColumnCopyingEstimator. 
ColumnSelectingEstimator 
Keeps or drops selected columns from an IDataView. 
ColumnSelectingTransformer 
ITransformer resulting from fitting an ColumnSelectingEstimator. 
CountFeatureSelectingEstimator 
Selects the slots for which the count of nondefault values is greater than or equal to a threshold. 
CustomMappingEstimator<TSrc,TDst> 
Applies a custom mapping function to the specified input columns. The result will be in output columns. 
CustomMappingFactory<TSrc,TDst> 
The base type for custom mapping factories. 
CustomMappingFactoryAttributeAttribute 
Place this attribute onto a type to cause it to be considered a custom mapping factory. 
CustomMappingTransformer<TSrc,TDst> 
ITransformer resulting from fitting an CustomMappingEstimator<TSrc,TDst>. 
DnnEstimator 
The DnnTransformer is used in following two scenarios.

DnnModel 
This class holds the information related to TensorFlow model and session. It provides some convenient methods to query model schema as well as creation of DnnEstimator object. 
DnnTransformer 
ITransformer for the DnnEstimator. 
FeatureContributionCalculatingEstimator 
Estimator for FeatureContributionCalculatingTransformer. Computes modelspecific perfeature contributions to the score of each input vector. 
FeatureContributionCalculatingTransformer 
ITransformer resulting from fitting a FeatureContributionCalculatingEstimator. 
GaussianKernel 
The Gaussian kernel. 
GlobalContrastNormalizingEstimator 
Normalizes (scales) vectors in the input column applying the global contrast normalization. 
HashingEstimator 
Estimator for HashingTransformer, which hashes either single valued columns or vector columns. For vector columns, it hashes each slot separately. 
HashingTransformer 
ITransformer resulting from fitting a HashingEstimator. 
KernelBase 
This class indicates which kernel should be approximated by the ApproximatedKernelTransformer.

KeyToBinaryVectorMappingEstimator 
Estimator for KeyToBinaryVectorMappingTransformer. Converts key types to their corresponding binary representation of the original value. 
KeyToBinaryVectorMappingTransformer 
ITransformer resulting from fitting a KeyToBinaryVectorMappingEstimator. 
KeyToValueMappingEstimator 
Estimator for KeyToValueMappingTransformer. Converts the key types back to their original values. 
KeyToValueMappingTransformer 
ITransformer resulting from fitting a KeyToValueMappingEstimator. 
KeyToVectorMappingEstimator 
Estimator for KeyToVectorMappingTransformer. Maps the value of a key into a knownsized vector of Single. 
KeyToVectorMappingTransformer 
ITransformer resulting from fitting a KeyToVectorMappingEstimator. 
LaplacianKernel 
The Laplacian kernel. 
LpNormNormalizingEstimator 
Normalizes (scales) vectors in the input column to the unit norm. The type of norm that is used can be specified by the user. 
LpNormNormalizingEstimatorBase 
Base estimator class for LpNormNormalizingEstimator and GlobalContrastNormalizingEstimator normalizers. 
LpNormNormalizingTransformer 
ITransformer resulting from fitting a LpNormNormalizingEstimator or GlobalContrastNormalizingEstimator. 
MissingValueIndicatorEstimator 
IEstimator<TTransformer> for the MissingValueIndicatorTransformer. 
MissingValueIndicatorTransformer 
ITransformer resulting from fitting a MissingValueIndicatorEstimator. 
MissingValueReplacingEstimator 
IEstimator<TTransformer> for the MissingValueReplacingTransformer. 
MissingValueReplacingTransformer 
ITransformer resulting from fitting a MissingValueReplacingEstimator. 
MutualInformationFeatureSelectingEstimator 
Selects the top k slots across all specified columns ordered by their mutual information with the label column (what you can learn about the label by observing the value of the specified column). 
NormalizingEstimator  
NormalizingTransformer 
ITransformer resulting from fitting an NormalizingEstimator. 
NormalizingTransformer.AffineNormalizerModelParameters<TData> 
The model parameters generated by affine normalization transformations. 
NormalizingTransformer.BinNormalizerModelParameters<TData> 
The model parameters generated by buckettizing the data into bins with monotonically increasing UpperBounds. The Density value is constant from bin to bin, for most cases. /// 
NormalizingTransformer.CdfNormalizerModelParameters<TData> 
The model parameters generated by cumulative distribution normalization transformations. The cumulative density function is parameterized by Mean and the StandardDeviation as observed during fitting. 
NormalizingTransformer.NormalizerModelParametersBase 
Base class for all the data normalizer models like NormalizingTransformer.AffineNormalizerModelParameters<TData>, NormalizingTransformer.BinNormalizerModelParameters<TData>, NormalizingTransformer.CdfNormalizerModelParameters<TData>. 
OneHotEncodingEstimator 
Converts one or more input columns of categorical values into as many output columns of onehot encoded vectors. 
OneHotEncodingTransformer 
ITransformer resulting from fitting a OneHotEncodingEstimator. 
OneHotHashEncodingEstimator 
Converts one or more input columns of categorical values into as many output columns of hashbased onehot encoded vectors. 
OneHotHashEncodingTransformer 
ITransformer resulting from fitting a OneHotHashEncodingEstimator. 
PrincipalComponentAnalysisTransformer 
PCA is a dimensionalityreduction transform which computes the projection of the feature vector onto a lowrank subspace. 
PrincipalComponentAnalyzer 
PCA is a dimensionalityreduction transform which computes the projection of the feature vector onto a lowrank subspace. 
TensorFlowEstimator 
The TensorFlowTransformer is used in following two scenarios.

TensorFlowModel 
This class holds the information related to TensorFlow model and session. It provides some convenient methods to query model schema as well as creation of TensorFlowEstimator object. 
TensorFlowTransformer 
ITransformer for the TensorFlowEstimator. 
TransformInputBase 
The base class for all transform inputs. 
TypeConvertingEstimator 
Estimator for KeyToVectorMappingTransformer. Converts the underlying input column type to a new type. The input and output column types need to be compatible. PrimitiveDataViewType 
TypeConvertingTransformer 
ITransformer resulting from fitting a TypeConvertingEstimator. 
ValueMappingEstimator 
Estimator for ValueMappingTransformer creating a keyvalue map using the pairs of values in the input data PrimitiveDataViewType 
ValueMappingEstimator<TKey,TValue> 
Estimator for ValueMappingTransformer creating a keyvalue map using the pairs of values in the input data PrimitiveDataViewType 
ValueMappingTransformer 
ITransformer resulting from fitting a ValueMappingEstimator. 
ValueToKeyMappingEstimator 
Estimator for ValueToKeyMappingTransformer. Converts input values (words, numbers, etc.) KeyDataViewType. 
ValueToKeyMappingTransformer 
ITransformer resulting from fitting a ValueToKeyMappingEstimator. 
VectorWhiteningEstimator  
VectorWhiteningTransformer 
Enums
DnnEstimator.Architecture 
Image classification model. 
DnnEstimator.DnnFramework 
Backend DNN training framework. 
LpNormNormalizingEstimatorBase.NormFunction 
The kind of unit norm vectors are rescaled to. This enumeration is serialized. 
MissingValueReplacingEstimator.ReplacementMode 
The possible ways to replace missing values. 
OneHotEncodingEstimator.OutputKind  
ValueToKeyMappingEstimator.KeyOrdinality 
Controls how the order of the output keys. 
WhiteningKind 
Which vector whitening technique to use. ZCA whitening ensures that the average covariance between whitened and original variables is maximal. In contrast, PCA whitening lead to maximally compressed whitened variables, as measured by squared covariance. 
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