Extract a list of features:

Extract from a list of examples with multiple features:

Extract features on a mixed-type dataset:

Extract features from a dataset formatted as a list of associations:

Extract features from data given as feature lists:

Feature extract from a Tabular:

Feature extract from a Dataset:

Extract features from a dataset that contains missing values:

Extract features from a single example using an extractor that requires no training:

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

Extract features using the "SentenceVector" method followed by dimension reduction "DimensionReducedVector":

Extract features on texts and images using the text-only "TFIDF" method:

Features have only been extracted from the text part, since "TFIDF" does not apply to images.

Specify feature extraction on multiple features by position:

Extract features with the "IndicatorVector" method on the second nominal variable only:

Use the Identity extractor method to copy the first variable as well:

A variable can be copied multiple times:

Specify the feature extraction on multiple features by key:

Generate a feature extractor using a custom function:

Chain the custom extractor with the "StandardizedVector" method:

Conform data prior to processing:

Extract features on textual data:

Compute term frequency-inverse document frequency vectors from texts:

By default, texts will be segmented into words. This gives the same result:

Extract features with the "IndicatorVector" method on nominal variables:

Extract features from a list of DateObject instances:

Train a feature extractor on a list of Graph instances:

Train a feature extractor on a list of TimeSeries instances:

Extract features from Molecule data:

Extract features from a selection of Audio instances:

Extract features from a dataset that contains missing values:

Use FeatureNames to name features, and refer to their names in part specifications:

Extract features with the "IndicatorVector" method on a simple dataset:

The first feature has been interpreted as numerical, and 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:

Construct a dataset of dog images:

Extract features from this dataset:

Reduce the dimension of the extracted vectors to 2:

Visualize the images at their feature positions:

A similar visualization can be directly obtained using FeatureSpacePlot: