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The ML.FEATURE_INFO functionThis document describes the ML.FEATURE_INFO function, which lets you see information about the input features that are used to train a model.
ML.FEATURE_INFO(MODEL `PROJECT_ID.DATASET.MODEL_NAME`)Arguments
ML.FEATURE_INFO takes the following arguments:
PROJECT_ID: Your project ID.DATASET: The BigQuery dataset that contains the model.MODEL_NAME: The name of the model.ML.FEATURE_INFO returns the following columns:
input: a STRING value that contains the name of the column in the input training data.min: a FLOAT64 value that contains the minimum value in the input column. min is NULL for non-numeric inputs.max: a FLOAT64 value that contains the maximum value in the input column. max is NULL for non-numeric inputs.mean: a FLOAT64 value that contains the average value for the input column. mean is NULL for non-numeric inputs.median: a FLOAT64 value that contains the median value for the input column. median is NULL for non-numeric inputs.stddev: a FLOAT64 value that contains the standard deviation value for the input column. stddev is NULL for non-numeric inputs.category_count: an INT64 value that contains the number of categories in the input column. category_count is NULL for non-categorical columns.null_count: an INT64 value that contains the number of NULL values in the input column.dimension: an INT64 value that contains the dimension of the input column if the input column has a ARRAY<STRUCT> type. dimension is NULL for non-ARRAY<STRUCT> columns.For matrix factorization models, only category_count is calculated for the user and item columns.
If you used the TRANSFORM clause in the CREATE MODEL statement that created the model, ML.FEATURE_INFO outputs the information of the pre-transform columns from the query_statement argument.
You must have the bigquery.models.create and bigquery.models.getData Identity and Access Management (IAM) permissions in order to run ML.FEATURE_INFO.
ML.FEATURE_INFO doesn't support imported TensorFlow models.
The following example retrieves feature information from the model mydataset.mymodel in your default project:
SELECT * FROM ML.FEATURE_INFO(MODEL `mydataset.mymodel`)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 2025-08-07 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-07 UTC."],[[["`ML.FEATURE_INFO` is a function that provides details about the input features used to train a model."],["The function syntax is `ML.FEATURE_INFO(MODEL project_id.dataset.model)`, requiring the project ID, dataset, and model name as arguments."],["The output includes columns such as `input`, `min`, `max`, `mean`, `median`, `stddev`, `category_count`, `null_count`, and `dimension`, providing insights into the characteristics of each input feature."],["Specific rules apply for `matrix factorization` models, they only calculate `category_count` for the `user` and `item` columns, and the function also shows information on the pre-transform columns when the `TRANSFORM` clause is used."],["Running `ML.FEATURE_INFO` requires `bigquery.models.create` and `bigquery.models.getData` permissions, and it does not support imported TensorFlow models."]]],[]]
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