The ML.LABEL_ENCODER function

This document describes theML.LABEL_ENCODER function, which you can use toencode a string expression to anINT64 value in[0, <number of categories>].

The encoding vocabulary is sorted alphabetically.NULL values and categoriesthat aren't in the vocabulary are encoded to0.

When used in theTRANSFORM clause,the vocabulary values calculated during training, alongwith the topk and frequency threshold values that you specified, areautomatically used in prediction.

You can use this function with models that supportmanual feature preprocessing. For moreinformation, see the following documents:

• End-to-end user journeys for ML models
• Contribution analysis user journey

Syntax

ML.LABEL_ENCODER(string_expression [, top_k] [, frequency_threshold]) OVER()

ML.LABEL_ENCODER takes the following arguments:

• string_expression: theSTRING expression to encode.
• top_k: anINT64 value that specifies the number of categoriesincluded in the encoding vocabulary. The function selects thetop_kmost frequent categories in the data and uses those; categories below thisthreshold are encoded to0. This value must be less than1,000,000to avoid problems due to high dimensionality. The default value is32,000.
• frequency_threshold: anINT64 value that limits the categoriesincluded in the encoding vocabulary based on category frequency. Thefunction uses categories whose frequency is greater than or equal tofrequency_threshold; categories below this threshold are encoded to0.The default value is5.

Output

ML.LABEL_ENCODER returns anINT64 value that represents the encodedstring expression.

Example

The following example performs label encoding on a set of string expressions.It limits the encoding vocabulary to the two categories that occur the mostfrequently in the data and that also occur two or more times.

SELECTf,ML.LABEL_ENCODER(f,2,2)OVER()ASoutputFROMUNNEST([NULL,'a','b','b','c','c','c','d','d'])ASfORDERBYf;

The output looks similar to the following:

+------+--------+|  f   | output |+------+--------+| NULL |      0 || a    |      0 || b    |      1 || b    |      1 || c    |      2 || c    |      2 || c    |      2 || d    |      0 || d    |      0 |+------+--------+

What's next

• For information about feature preprocessing, seeFeature preprocessing overview.
• For information about the supported SQL statements and functions for each