Feature Vector

The array of feature values comprising an example. The feature vector is input during training and during inference. For example, the feature vector for a model with two discrete features might be:

[0.92, 0.56]

Each example supplies different values for the feature vector, so the feature vector for the next example could be something like:

[0.73, 0.49]

Feature engineering determines how to represent features in the feature vector. For example, a binary categorical feature with five possible values might be represented with one-hot encoding. In this case, the portion of the feature vector for a particular example would consist of four zeroes and a single 1.0 in the third position, as follows:

[0.0, 0.0, 1.0, 0.0, 0.0]

As another example, suppose your model consists of three features:

a binary categorical feature with five possible values represented with one-hot encoding; for example: [0.0, 1.0, 0.0, 0.0, 0.0]

another binary categorical feature with three possible values represented with one-hot encoding; for example: [0.0, 0.0, 1.0]

a floating-point feature; for example: 8.3.

In this case, the feature vector for each example would be represented by nine values. Given the example values in the preceding list, the feature vector would be:

0.0

1.0

0.0

1.0

8.3

See Numerical data: How a model ingests data using feature vectors in Machine Learning Crash Course for more information.

Real-world uses

Created for this library

1.
A retail recommendation team builds a feature vector per user combining demographic, behavioral, and embedding features for its ranker.
2.
A credit team produces one feature vector per application that flows through preprocessing, scoring, and explanation in production.
3.
A search-quality team produces feature vectors per query-document pair, combining textual and behavioral features for ranking.

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The array of feature values comprising an example. The feature vector is input during training and during inference. For example, the feature vector for a model with two discrete features might be:

[0.92, 0.56]

Each example supplies different values for the feature vector, so the feature vector for the next example could be something like:

[0.73, 0.49]

[0.0, 0.0, 1.0, 0.0, 0.0]

As another example, suppose your model consists of three features:

a binary categorical feature with five possible values represented with one-hot encoding; for example: [0.0, 1.0, 0.0, 0.0, 0.0]

another binary categorical feature with three possible values represented with one-hot encoding; for example: [0.0, 0.0, 1.0]

a floating-point feature; for example: 8.3.

In this case, the feature vector for each example would be represented by nine values. Given the example values in the preceding list, the feature vector would be:

0.0

1.0

0.0

1.0

8.3

See Numerical data: How a model ingests data using feature vectors in Machine Learning Crash Course for more information.

Real-world uses

Created for this library

1.
A retail recommendation team builds a feature vector per user combining demographic, behavioral, and embedding features for its ranker.
2.
A credit team produces one feature vector per application that flows through preprocessing, scoring, and explanation in production.
3.
A search-quality team produces feature vectors per query-document pair, combining textual and behavioral features for ranking.

Back to glossary

Feature Vector

Real-world uses

Related terms

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Feature Vector

Real-world uses

Related terms