title	Data transformations
description	Explore the feature engineering components supported in ML.NET.
ms.date	09/14/2022
ms.custom	sfi-ropc-nochange

Data transformations

Data transformations are used to:

• Prepare data for model training.
• Apply an imported model in TensorFlow or ONNX format.
• Post-process data after it has been passed through a model.

The transformations in this guide return classes that implement the IEstimator interface. Data transformations can be chained together. Each transformation both expects and produces data of specific types and formats, which are specified in the linked reference documentation.

Some data transformations require training data to calculate their parameters. For example: the xref:Microsoft.ML.NormalizationCatalog.NormalizeMeanVariance* transformer calculates the mean and variance of the training data during the Fit() operation, and uses those parameters in the Transform() operation.

Other data transformations don't require training data. For example: the xref:Microsoft.ML.ImageEstimatorsCatalog.ConvertToGrayscale* transformation can perform the Transform() operation without having seen any training data during the Fit() operation.

Column mapping and grouping

Transform	Definition	ONNX Exportable
xref:Microsoft.ML.TransformExtensionsCatalog.Concatenate*	Concatenate one or more input columns into a new output column	Yes
xref:Microsoft.ML.TransformExtensionsCatalog.CopyColumns*	Copy and rename one or more input columns	Yes
xref:Microsoft.ML.TransformExtensionsCatalog.DropColumns*	Drop one or more input columns	Yes
xref:Microsoft.ML.TransformExtensionsCatalog.SelectColumns*	Select one or more columns to keep from the input data	Yes

Normalization and scaling

Transform	Definition	ONNX Exportable
xref:Microsoft.ML.NormalizationCatalog.NormalizeMeanVariance*	Subtract the mean (of the training data) and divide by the variance (of the training data)	Yes
xref:Microsoft.ML.NormalizationCatalog.NormalizeLogMeanVariance*	Normalize based on the logarithm of the training data	Yes
xref:Microsoft.ML.NormalizationCatalog.NormalizeLpNorm*	Scale input vectors by their lp-norm, where p is 1, 2 or infinity. Defaults to the l2 (Euclidean distance) norm	Yes
xref:Microsoft.ML.NormalizationCatalog.NormalizeGlobalContrast*	Scale each value in a row by subtracting the mean of the row data and divide by either the standard deviation or l2-norm (of the row data), and multiply by a configurable scale factor (default 2)	Yes
xref:Microsoft.ML.NormalizationCatalog.NormalizeBinning*	Assign the input value to a bin index and divide by the number of bins to produce a float value between 0 and 1. The bin boundaries are calculated to evenly distribute the training data across bins	Yes
xref:Microsoft.ML.NormalizationCatalog.NormalizeSupervisedBinning*	Assign the input value to a bin based on its correlation with label column	Yes
xref:Microsoft.ML.NormalizationCatalog.NormalizeMinMax*	Scale the input by the difference between the minimum and maximum values in the training data	Yes
xref:Microsoft.ML.NormalizationCatalog.NormalizeRobustScaling*	Scale each value using statistics that are robust to outliers that will center the data around 0 and scales the data according to the quantile range.	Yes

Conversions between data types

Transform	Definition	ONNX Exportable
xref:Microsoft.ML.ConversionsExtensionsCatalog.ConvertType*	Convert the type of an input column to a new type	Yes
xref:Microsoft.ML.ConversionsExtensionsCatalog.MapValue*	Map values to keys (categories) based on the supplied dictionary of mappings	No
xref:Microsoft.ML.ConversionsExtensionsCatalog.MapValueToKey*	Map values to keys (categories) by creating the mapping from the input data	Yes
xref:Microsoft.ML.ConversionsExtensionsCatalog.MapKeyToValue*	Convert keys back to their original values	Yes
xref:Microsoft.ML.ConversionsExtensionsCatalog.MapKeyToVector*	Convert keys back to vectors of original values	Yes
xref:Microsoft.ML.ConversionsCatalog.MapKeyToBinaryVector*	Convert keys back to a binary vector of original values	No
xref:Microsoft.ML.ConversionsExtensionsCatalog.Hash*	Hash the value in the input column	Yes

Text transformations

Transform	Definition	ONNX Exportable
xref:Microsoft.ML.TextCatalog.FeaturizeText*	Transform a text column into a float array of normalized ngrams and char-grams counts	No
xref:Microsoft.ML.TextCatalog.TokenizeIntoWords*	Split one or more text columns into individual words	Yes
xref:Microsoft.ML.TextCatalog.TokenizeIntoCharactersAsKeys*	Split one or more text columns into individual characters floats over a set of topics	Yes
xref:Microsoft.ML.TextCatalog.NormalizeText*	Change case, remove diacritical marks, punctuation marks, and numbers	Yes
xref:Microsoft.ML.TextCatalog.ProduceNgrams*	Transform text column into a bag of counts of ngrams (sequences of consecutive words)	Yes
xref:Microsoft.ML.TextCatalog.ProduceWordBags*	Transform text column into a bag of counts of ngrams vector	Yes
xref:Microsoft.ML.TextCatalog.ProduceHashedNgrams*	Transform text column into a vector of hashed ngram counts	No
xref:Microsoft.ML.TextCatalog.ProduceHashedWordBags*	Transform text column into a bag of hashed ngram counts	Yes
xref:Microsoft.ML.TextCatalog.RemoveDefaultStopWords*	Remove default stop words for the specified language from input columns	Yes
xref:Microsoft.ML.TextCatalog.RemoveStopWords*	Removes specified stop words from input columns	Yes
xref:Microsoft.ML.TextCatalog.LatentDirichletAllocation*	Transform a document (represented as a vector of floats) into a vector of floats over a set of topics	Yes
xref:Microsoft.ML.TextCatalog.ApplyWordEmbedding*	Convert vectors of text tokens into sentence vectors using a pretrained model	Yes

Image transformations

Transform	Definition	ONNX Exportable
xref:Microsoft.ML.ImageEstimatorsCatalog.ConvertToGrayscale*	Convert an image to grayscale	No
xref:Microsoft.ML.ImageEstimatorsCatalog.ConvertToImage*	Convert a vector of pixels to xref:Microsoft.ML.Transforms.Image.ImageDataViewType	No
xref:Microsoft.ML.ImageEstimatorsCatalog.ExtractPixels*	Convert pixels from input image into a vector of numbers	No
xref:Microsoft.ML.ImageEstimatorsCatalog.LoadImages*	Load images from a folder into memory	No
xref:Microsoft.ML.ImageEstimatorsCatalog.LoadRawImageBytes*	Loads images of raw bytes into a new column.	No
xref:Microsoft.ML.ImageEstimatorsCatalog.ResizeImages*	Resize images	No
xref:Microsoft.ML.OnnxCatalog.DnnFeaturizeImage*	Applies a pretrained deep neural network (DNN) model to transform an input image into a feature vector	No

Categorical data transformations

Transform	Definition	ONNX Exportable
xref:Microsoft.ML.CategoricalCatalog.OneHotEncoding*	Convert one or more text columns into one-hot encoded vectors	Yes
xref:Microsoft.ML.CategoricalCatalog.OneHotHashEncoding*	Convert one or more text columns into hash-based one-hot encoded vectors	No

Time series data transformations

Transform	Definition	ONNX Exportable
xref:Microsoft.ML.TimeSeriesCatalog.DetectAnomalyBySrCnn*	Detect anomalies in the input time series data using the Spectral Residual (SR) algorithm	No
xref:Microsoft.ML.TimeSeriesCatalog.DetectChangePointBySsa*	Detect change points in time series data using singular spectrum analysis (SSA)	No
xref:Microsoft.ML.TimeSeriesCatalog.DetectIidChangePoint*	Detect change points in independent and identically distributed (IID) time series data using adaptive kernel density estimations and martingale scores	No
xref:Microsoft.ML.TimeSeriesCatalog.ForecastBySsa*	Forecast time series data using singular spectrum analysis (SSA)	No
xref:Microsoft.ML.TimeSeriesCatalog.DetectSpikeBySsa*	Detect spikes in time series data using singular spectrum analysis (SSA)	No
xref:Microsoft.ML.TimeSeriesCatalog.DetectIidSpike*	Detect spikes in independent and identically distributed (IID) time series data using adaptive kernel density estimations and martingale scores	No
xref:Microsoft.ML.TimeSeriesCatalog.DetectEntireAnomalyBySrCnn*	Detect anomalies for the entire input data using the SRCNN algorithm.	No
xref:Microsoft.ML.TimeSeriesCatalog.DetectSeasonality*	Detect seasonality using fourier analysis.	No
xref:Microsoft.ML.TimeSeriesCatalog.LocalizeRootCause*	Localizes root cause from time series input using a decision tree algorithm.	No
xref:Microsoft.ML.TimeSeriesCatalog.LocalizeRootCauses*	Localizes root causes from tie series input.	No

Missing values

Transform	Definition	ONNX Exportable
xref:Microsoft.ML.ExtensionsCatalog.IndicateMissingValues*	Create a new boolean output column, the value of which is true when the value in the input column is missing	Yes
xref:Microsoft.ML.ExtensionsCatalog.ReplaceMissingValues*	Create a new output column, the value of which is set to a default value if the value is missing from the input column, and the input value otherwise	Yes

Feature selection

Transform	Definition	ONNX Exportable
xref:Microsoft.ML.FeatureSelectionCatalog.SelectFeaturesBasedOnCount*	Select features whose non-default values are greater than a threshold	Yes
xref:Microsoft.ML.FeatureSelectionCatalog.SelectFeaturesBasedOnMutualInformation*	Select the features on which the data in the label column is most dependent	Yes

Feature transformations

Transform	Definition	ONNX Exportable
xref:Microsoft.ML.KernelExpansionCatalog.ApproximatedKernelMap*	Map each input vector onto a lower dimensional feature space, where inner products approximate a kernel function, so that the features can be used as inputs to the linear algorithms	No
xref:Microsoft.ML.PcaCatalog.ProjectToPrincipalComponents*	Reduce the dimensions of the input feature vector by applying the Principal Component Analysis algorithm

Explainability transformations

Transform	Definition	ONNX Exportable
xref:Microsoft.ML.ExplainabilityCatalog.CalculateFeatureContribution*	Calculate contribution scores for each element of a feature vector	No

Calibration transformations

Transform	Definition	ONNX Exportable
xref:Microsoft.ML.BinaryClassificationCatalog.CalibratorsCatalog.Platt%28System.String%2CSystem.String%2CSystem.String%29	Transforms a binary classifier raw score into a class probability using logistic regression with parameters estimated using the training data	Yes
xref:Microsoft.ML.BinaryClassificationCatalog.CalibratorsCatalog.Platt%28System.Double%2CSystem.Double%2CSystem.String%29	Transforms a binary classifier raw score into a class probability using logistic regression with fixed parameters	Yes
xref:Microsoft.ML.BinaryClassificationCatalog.CalibratorsCatalog.Naive*	Transforms a binary classifier raw score into a class probability by assigning scores to bins, and calculating the probability based on the distribution among the bins	Yes
xref:Microsoft.ML.BinaryClassificationCatalog.CalibratorsCatalog.Isotonic*	Transforms a binary classifier raw score into a class probability by assigning scores to bins, where the position of boundaries and the size of bins are estimated using the training data	No

Deep learning transformations

Transform	Definition	ONNX Exportable
xref:Microsoft.ML.OnnxCatalog.ApplyOnnxModel*	Transform the input data with an imported ONNX model	No
xref:Microsoft.ML.TensorflowCatalog.LoadTensorFlowModel*	Transform the input data with an imported TensorFlow model	No

Custom transformations

Transform	Definition	ONNX Exportable
xref:Microsoft.ML.CustomMappingCatalog.FilterByCustomPredicate*	Drops rows where a specified predicate returns true.	No
xref:Microsoft.ML.CustomMappingCatalog.FilterByStatefulCustomPredicate*	Drops rows where a specified predicate returns true, but allows for a specified state.	No
xref:Microsoft.ML.CustomMappingCatalog.CustomMapping*	Transform existing columns onto new ones with a user-defined mapping	No
xref:Microsoft.ML.ExpressionCatalog.Expression*	Apply an expression to transform columns into new ones	No