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FeatureExtractorFunction[…]

represents a feature extractor function generated by FeatureExtraction.

Details and Options
Details and Options Details and Options
Examples  
Basic Examples  
Scope  
Possible Issues  
See Also
Related Guides
History
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FeatureExtractorFunction

FeatureExtractorFunction[…]

represents a feature extractor function generated by FeatureExtraction.

Details and Options

  • FeatureExtractorFunction works like Function.
  • FeatureExtractorFunction[…][data] extracts features from data.
  • FeatureExtractorFunction[…][{data1,data2,…}] extracts features from each of the datai.
  • FeatureExtractorFunction[…][data,prop] gives the specified property of the feature extraction associated with data.
  • Possible properties include:
  • "ExtractedFeatures"features extracted from data (default)
    "OriginalData"deduce original data from extracted features
    "ReconstructedData"extraction and inverse extraction of data
  • The following options can be given:
  • PerformanceGoalAutomaticaspects of performance to try to optimize
    RandomSeeding1234what seeding of pseudorandom generators should be done internally
  • Possible settings for RandomSeeding include:
  • Automaticautomatically reseed every time the function is called
    Inheriteduse externally seeded random numbers
    seeduse an explicit integer or strings as a seed

Examples

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Basic Examples  (1)

Train a FeatureExtractorFunction on a simple dataset:

Wolfram Language code: fe = FeatureExtraction[{{1.4, "A"}, {1.5, "A"}, {2.3, "B"}, {5.4, "B"}}]

Extract features from a new example:

Wolfram Language code: fe[{2.4, "A"}]

Extract features from a list of examples:

Wolfram Language code: fe[{{2.4, "A"}, {3.7, "B"}}]

Scope  (5)

Some feature extractors can only perform an approximation of the inverse extraction:

Wolfram Language code: fe = FeatureExtraction[{{1.4, 1.4, 5.4, 5.2}, {1.5, 1.5, 6.4, 5.2}, {1.2, 1.2, 6.2, 5.2}, {1.6, 1.6, 4.3, 5.2}}, "DimensionReducedVector"]
Wolfram Language code: features = fe[{1.1, 1.2, 5.1, 5.7}]
Wolfram Language code: fe[features, "OriginalData"]

The FeatureExtraction property "ReconstructedData" can be used to obtain the data after extraction and reconstruction:

Wolfram Language code: FeatureExtraction[{{1.4, 1.4, 5.4, 5.2}, {1.5, 1.5, 6.4, 5.2}, {1.2, 1.2, 6.2, 5.2}, {1.6, 1.6, 4.3, 5.2}}, "DimensionReducedVector", "ReconstructedData"]

Some feature extractors cannot be inverted:

Wolfram Language code: FeatureExtraction[{[image], [image] , [image], [image]}, "ImageFeatures", "ReconstructedData"]

Train a feature extractor from a dataset that contains missing values:

Wolfram Language code: fe = FeatureExtraction[{{1.4, Missing[], "A"}, {1.5, 50.2, "A"}, {Missing[], 42.3, "B"}, {5.4, 61.7, "B"}}]

The feature extractor now indicates that missing values are imputed. The feature extractor can extract features even when values are missing:

Wolfram Language code: fe[{3.4, Missing[], "B"}]

Get Information from a trained FeatureExtractorFunction:

Wolfram Language code: Information[FeatureExtractorFunction[Association["ExampleNumber" -> 4, "Preprocessor" -> MachineLearning`MLProcessor["ToMLDataset", Association["Input" -> Association["age" -> Association["Type" -> "Numerical"], "gender" -> Association["Type" - ... ate" -> DateObject[{2025, 5, 2, 13, 0, 39.042795`8.344115878952366}, "Instant", "Gregorian", 2.], "ProcessorCount" -> 10, "ProcessorType" -> "ARM64", "OperatingSystem" -> "MacOSX", "SystemWordLength" -> 64, "Evaluations" -> {}]]]]

Find the available properties:

Wolfram Language code: Information[FeatureExtractorFunction[Association["ExampleNumber" -> 4, "Preprocessor" -> MachineLearning`MLProcessor["ToMLDataset", Association["Input" -> Association["age" -> Association["Type" -> "Numerical"], "gender" -> Association["Type" - ... ate" -> DateObject[{2025, 5, 2, 13, 0, 39.042795`8.344115878952366}, "Instant", "Gregorian", 2.], "ProcessorCount" -> 10, "ProcessorType" -> "ARM64", "OperatingSystem" -> "MacOSX", "SystemWordLength" -> 64, "Evaluations" -> {}]]], "Properties"]

Get information about the feature names and training examples:

Wolfram Language code: Information[FeatureExtractorFunction[Association["ExampleNumber" -> 4, "Preprocessor" -> MachineLearning`MLProcessor["ToMLDataset", Association["Input" -> Association["age" -> Association["Type" -> "Numerical"], "gender" -> Association["Type" - ... ate" -> DateObject[{2025, 5, 2, 13, 0, 39.042795`8.344115878952366}, "Instant", "Gregorian", 2.], "ProcessorCount" -> 10, "ProcessorType" -> "ARM64", "OperatingSystem" -> "MacOSX", "SystemWordLength" -> 64, "Evaluations" -> {}]]], {"FeatureNames", "ExampleNumber"}]

Possible Issues  (1)

Retrieve a old FeatureExtractorFunction using keyed features:

Wolfram Language code: fe = FeatureExtractorFunction[Association["ExampleNumber" -> 0, "Preprocessor" -> MachineLearning`MLProcessor["ToMLDataset", Association["Input" -> Association["input" -> Association["Type" -> "Text"]], "Output" -> Association["f1" -> Asso ... "Date" -> DateObject[{2026, 1, 28, 15, 36, 22.701151`8.10862286062899}, "Instant", "Gregorian", 0.], "ProcessorCount" -> 10, "ProcessorType" -> "ARM64", "OperatingSystem" -> "MacOSX", "SystemWordLength" -> 64, "Evaluations" -> {}]]];

Using absent or incorrect feature names will give errors or missing content:

Wolfram Language code: fe["Hello"]
Wolfram Language code: fe[<|"Input" -> "Hello"|>]

Retrieve the feature names using Information:

Wolfram Language code: Information[fe, "FeatureNames"]

Correct the feature names:

Wolfram Language code: fe[<|"input" -> "Hello"|>]
Wolfram Research (2016), FeatureExtractorFunction, Wolfram Language function, https://reference.wolfram.com/language/ref/FeatureExtractorFunction.html (updated 2017).

Text

Wolfram Research (2016), FeatureExtractorFunction, Wolfram Language function, https://reference.wolfram.com/language/ref/FeatureExtractorFunction.html (updated 2017).

CMS

Wolfram Language. 2016. "FeatureExtractorFunction." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2017. https://reference.wolfram.com/language/ref/FeatureExtractorFunction.html.

APA

Wolfram Language. (2016). FeatureExtractorFunction. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/FeatureExtractorFunction.html

BibTeX

@misc{reference.wolfram_2026_featureextractorfunction, author="Wolfram Research", title="{FeatureExtractorFunction}", year="2017", howpublished="\url{https://reference.wolfram.com/language/ref/FeatureExtractorFunction.html}", note=[Accessed: 15-June-2026]}

BibLaTeX

@online{reference.wolfram_2026_featureextractorfunction, organization={Wolfram Research}, title={FeatureExtractorFunction}, year={2017}, url={https://reference.wolfram.com/language/ref/FeatureExtractorFunction.html}, note=[Accessed: 15-June-2026]}