The ML.CENTROIDS function

This document describes the ML.CENTROIDS function, which lets you return information about the centroids in a k-means model.

Syntax

ML.CENTROIDS(
 MODEL `PROJECT_ID.DATASET.MODEL`,
 STRUCT([, STANDARDIZE AS standardize]))

Arguments

ML.CENTROIDS takes the following arguments:

  • PROJECT_ID: your project ID.
  • DATASET: the BigQuery dataset that contains the model.
  • MODEL: the name of the model.
  • STANDARDIZE: a BOOL value that specifies whether the centroid features should be standardized to assume that all features have a mean of 0 and a standard deviation of 1. Standardizing the features allows the absolute magnitude of the values to be compared to each other. The default value is FALSE.

Output

ML.CENTROIDS returns the following columns:

  • trial_id: an INT64 value that contains the hyperparameter tuning trial ID. This column is only returned if you ran hyperparameter tuning when creating the model.
  • centroid_id: an INT64 value that contains the centroid ID.
  • feature: a STRING value that contains the feature column name.
  • numerical_value: a FLOAT64 value that contains the feature value for the centroid that centroid_id identifies if the column identified by the feature value is numeric. Otherwise, numerical_value is NULL.

  • categorical_value: an ARRAY<STRUCT> value that contains information about categorical features. Each struct contains the following fields:

    • categorical_value.category: a STRING value that contains the name of each category.
    • categorical_value.value: a STRING value that contains the value of categorical_value.category for the centroid that centroid_id identifies.

  • geography_value: a STRING value that contains the categorical_value.category value for the centroid that centroid_id identifies if the column identified by the feature value is of type GEOGRAPHY. Otherwise, geography_value value is NULL.

The output contains one row per feature per centroid.

Examples

The following examples show how to use ML.CENTROIDS with and without the standardize argument.

Without standardization

Numerical features

The following example retrieves centroid information from the model mydataset.my_kmeans_model in your default project. This model only contains numerical features.

SELECT
*
FROM
ML.CENTROIDS(MODEL`mydataset.my_kmeans_model`)

This query returns results like the following:

+-------------+-------------------+----------------------+---------------------+
| centroid_id | feature | numerical_value | categorical_value |
+-------------+-------------------+----------------------+---------------------+
| 3 | x_coordinate | 3095929.0 | [] |
| 3 | y_coordinate | 1.0089726307692308E7 | [] |
| 2 | x_coordinate | 3117072.65625 | [] |
| 2 | y_coordinate | 1.0083220745833334E7 | [] |
| 1 | x_coordinate | 3259947.096227731 | [] |
| 1 | y_coordinate | 1.0105690227895036E7 | [] |
| 4 | x_coordinate | 3109887.9056603773 | [] |
| 4 | y_coordinate | 1.0057112358490566E7 | [] |
+-------------+-------------------+----------------------+---------------------+

Categorical features

The following example retrieves centroid information from the model mydataset.my_kmeans_model in your default project. This model contains categorical features.

SELECT
*
FROM
ML.CENTROIDS(MODEL`mydataset.my_kmeans_model`)
ORDERBY
centroid_id;

This query returns results like the following:

+-------------+-------------------+---------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| centroid_id | feature |numerical_value| categorical_value |
+-------------+-------------------+---------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 1 | department | NULL | [{"category":"Medieval Art","feature_value":"1.0"}] |
| 1 | medium | NULL | [{"category":"Iron","feature_value":"0.21602160216021601"},{"category":"Glass, ceramic","feature_value":"0.3933393339333933"},{"category":"Copper alloy","feature_value":"0.39063906390639064"}] |
| 2 | medium | NULL | [{"category":"Wood, gesso, paint","feature_value":"0.15"},{"category":"Carnelian","feature_value":"0.2692307692307692"},{"category":"Papyrus, ink","feature_value":"0.2653846153846154"},{"category":"Steatite, glazed","feature_value":"0.3153846153846154"}] |
| 2 | department | NULL | [{"category":"Egyptian Art","feature_value":"1.0"}] |
| 3 | medium | NULL | [{"category":"Faience","feature_value":"1.0"}] |
| 3 | department | NULL | [{"category":"Egyptian Art","feature_value":"1.0"}] |
| 4 | medium | NULL | [{"category":"Steatite","feature_value":"1.0"}] |
| 4 | department | NULL | [{"category":"Egyptian Art","feature_value":"1.0"}] |
| 5 | medium | NULL | [{"category":"Red quartzite","feature_value":"0.20316027088036118"},{"category":"Bronze or copper alloy","feature_value":"0.3476297968397291"},{"category":"Gold","feature_value":"0.4492099322799097"}] |
| 5 | department | NULL | [{"category":"Egyptian Art","feature_value":"1.0"}] |
+-------------+-------------------+---------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

Numerical and categorical features

The following are the results from the same query against a k-means model with both numerical and categorical features.

+-------------+--------------------+-------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| centroid_id | feature | numerical_value | categorical_value |
+-------------+--------------------+-------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 1 | start_station_name | NULL | [{"category":"Toomey Rd @ South Lamar","value":"0.5714285714285714"},{"category":"State Capitol @ 14th & Colorado","value":"0.42857142857142855"}] |
| 1 | duration_minutes | 9.142857142857142 | [] |
| 2 | duration_minutes | 9.0 | [] |
| 2 | start_station_name | NULL | [{"category":"Rainey @ River St","value":"0.14285714285714285"},{"category":"11th & San Jacinto","value":"0.42857142857142855"},{"category":"ACC - West & 12th Street","value":"0.14285714285714285"},{"category":"East 11th St. at Victory Grill","value":"0.2857142857142857"}] |
+-------------+--------------------+-------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

With standardization

The following example retrieves centroid information from the model mydataset.my_kmeans_model in your default project. The query in this example assumes that all features have a mean of 0 and a standard deviation of 1.

SELECT
*
FROM
ML.CENTROIDS(MODEL`mydataset.my_kmeans_model`,
STRUCT(TRUEASstandardize))

What's next

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025年10月24日 UTC.