ML.PRINCIPAL_COMPONENTS function lets you see the principal components.
Principal components and eigenvectors are the same concepts in a
For information about model weights support in BigQuery ML, see Model weights overview.
For information about the supported model types of each SQL statement and function, and for a list of all of the supported SQL statements and functions for each model type, read End-to-end user journey for each model.
project_id: your project ID
dataset: the BigQuery dataset that contains the model
model: the name of the model
ML.PRINCIPAL_COMPONENTS function returns the following columns:
- principal_component_id. An integer that identifies the principal component.
- feature. The column name that contains the feature.
- numerical_value. If
featureis numerical, the value of
featurefor the centroid that
featureis not numeric, the value is
- categorical_value. An ARRAY of STRUCTs containing information about
categorical features. Each STRUCT contains the following fields:
- categorical_value.category. The name of each category.
- categorical_value.value. The value of
categorical_value.categoryfor the centroid that
The principal components are ordered in the descending order of their associated eigenvalues, which can be retrieved by using the ml.principal_component_info function.
The following example retrieves the principal components from
mydataset. The dataset is in your default project.
SELECT * FROM ML.PRINCIPAL_COMPONENTS(MODEL `mydataset.mymodel`)