ML.FEATURE_INFO function allows you to see information about the input
features used to train a model.
For information about feature preprocessing support in BigQuery ML, see Feature preprocessing overview.
For information about supported model types of each SQL statement and function, and all supported SQL statements and functions for each model type, read End-to-end user journey for each model.
project_idis your project ID.
datasetis the BigQuery dataset that contains the model.
modelis the name of the model.
ML.FEATURE_INFO returns the following columns:
input— The name of the column in the input training data.
min— The sample minimum. This column is NULL for non-numeric inputs.
max— The sample maximum. This column is NULL for non-numeric inputs.
mean— The average. This column is NULL for non-numeric inputs.
stddev— The standard deviation. This column is NULL for non-numeric inputs.
category_count— The number of categories. This column is NULL for non-categorical columns.
null_count— The number of NULLs.
clause was present in the
CREATE MODEL statement that created
ML.FEATURE_INFO outputs the information of the pre-transform columns from
bigquery.models.getData are required to run
For matrix factorization
models, only the
category_count is calculated for the
The following example retrieves feature information from
mydataset. The dataset is in your default project.
SELECT * FROM ML.FEATURE_INFO(MODEL `mydataset.mymodel`)
ML.FEATURE_INFO function is subject to the following limitations:
ML.FEATURE_INFOdoes not support imported TensorFlow models.