Train a FeatureExtractorFunction on a simple dataset:

Extract features from a new example:

Extract features from a list of examples:

Train a feature extractor on a dataset of images:

Use the feature extractor on the training set:

Construct a feature extractor from a numerical dataset using the "StandardizedVector" extractor method:

Use the feature extractor on the training set:

The property "ExtractedFeatures" can be used to perform this operation in one step:

Multiple properties can be queried:

Train a feature extractor on textual data:

Use the feature extractor on new examples:

Train a feature extractor on a list of DateObject:

Extract features from a new DateObject:

A string date can also be given:

Train a feature extractor on a list of Graph:

Extract features from a new graph:

Train a feature extractor on a list of TimeSeries:

Extract features from a new TimeSeries:

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 text using the "TFIDF" method followed by the "DimensionReduced" method:

Extract features on the training set:

Generate a feature extractor using a custom function:

Apply the extractor on the training set:

Chain the custom extractor with the "StandardizedVector" method:

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

Extract features from a new example:

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:

Train a feature extractor on a mixed-type dataset:

Extract features from a new example:

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

Features will only be extracted from the text part:

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

This feature extractor can extract features even when values are missing:

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:

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

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

Some feature extractors cannot be inverted:

Train a feature extractor from a list of associations:

Extract features from a new example: