Train a feature extractor on a list of examples with a single feature:

Extract features from a new example:

Extract features from multiple new examples:

Train a feature extractor on a list of examples with multiple features:

Extract features from multiple new examples:

Train a feature extractor on a mixed-type dataset:

Extract features from a new example:

Train a feature extractor from a list of associations:

Extract features from a new example:

Extract features from multiple new examples:

Train a feature extractor from data given as feature lists:

Train a feature extractor from a Tabular:

Train a feature extractor from a Dataset:

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

Define a feature extractor that requires no training:

Apply it on some text:

Specify the feature extractor "SentenceVector" on a single textual feature:

Apply it on some text:

Train a feature extractor using the "StandardizedVector" method:

Extract features from a new example:

Since this feature extractor is invertible, the FeatureExtractorFunction property "OriginalData" can be used to perform the inverse extraction:

Train a feature extractor on text using the "TFIDF" method followed by the "DimensionReducedVector" method:

Extract features on the training set:

Train a feature extractor on texts and images using the text-only "TFIDF" method:

Features will only be extracted from the text part:

Specify the feature extraction on multiple features by position:

Use the feature extractor on new features:

A list of two items will be assumed to be a single input of two features:

Train a feature extractor with the "IndicatorVector" method on only the second nominal variable:

The first nominal variable is dropped:

Use the Identity extractor method to copy the first variable:

The first variable is copied:

A variable can be copied multiple times:

Specify the feature extraction on multiple features by key:

Use the feature extractor on new features:

Using the feature extractor on a list will assume the same ordering of features as originally specified:

Generate a feature extractor using a custom function:

Apply the extractor on the training set:

Chain the custom extractor with the "StandardizedVector" method:

Conform data prior to processing:

Reduce the dimensionality of the output:

Create a feature extractor for textual data using the "SentenceVector" extractor with no training:

Input type is inferred from the specified extractor. Use the feature extractor on some examples:

Create a feature extractor for examples with implicit textual and image features:

Features will be extracted from both parts:

Train a feature extractor on textual data:

Train a feature extractor with the "IndicatorVector" method on nominal variables:

Train a feature extractor to compute term frequency-inverse document frequency vectors from texts:

The term frequency-inverse document frequency matrix of the training set can be computed in a SparseArray:

Visualize the matrix:

The "TFIDF" method can also be used on tokenized data (nominal bags):

Train a feature extractor on a list of DateObject instances:

Extract features from a new DateObject:

A string date can also be given:

Train a feature extractor on a list of Graph instances:

Extract features from a new graph:

Train a feature extractor on a list of TimeSeries instances:

Train a feature extractor on Molecule data:

Train a feature extractor on a list of Audio instances:

Get Information from a trained FeatureExtractorFunction:

Find the available properties:

Get information about the input and output types:

Train a feature extractor and give a name to each feature:

Use the association format to extract features from a new example:

The list format can still be used:

Use FeatureNames to set up names and refer to them in FeatureExtraction[examples,{spec₁ext₁,…}]:

Extract features on a new example using the names to specify the features:

Train a feature extractor with the "IndicatorVector" method on a simple dataset:

The first feature has been interpreted as numerical. Since the "IndicatorVector" method only acts on nominal features, the first feature is unchanged:

Use FeatureTypes to enforce the interpretation of the first feature as nominal:

Now both features are encoded as indicator vectors:

Creating a feature extractor with no training infers the expected data type from the specific extractor:

Specifying the feature type will override the assumption:

Apply to named features:

Construct a dataset of dog images:

Train an extractor function from this dataset:

Generate a NearestFunction on the extracted features of the dataset:

Using the NearestFunction, construct a function that displays the nearest image of the dataset:

Use this function on images that are not in the dataset:

This feature extractor function can also be used to delete image pairs that are too similar:

Load the text of Alice in Wonderland:

Split the text into sentences:

Train a feature extractor on these sentences:

Generate a NearestFunction with the sentences' features:

Using the NearestFunction, construct a function that displays the nearest sentence in Alice in Wonderland:

Use this function with a few queries:

Load the "MNIST" dataset from ExampleData and keep the images:

Convert images to numerical data and separate the dataset into a training set and a test set:

The dimension of the dataset is 784:

Create a feature extractor using the "MissingImputed" method:

Replace some values of a test-set vector by Missing[] and visualize it:

Impute missing values using the FeatureExtractorFunction[…]:

Visualize the original image, the image with missing values, and the imputed image: