The ML.FEATURE_INFO function

This document describes the ML.FEATURE_INFO function, which lets you see information about the input features that are used to train a model.

For more information about which models support this function, see End-to-end user journeys for ML models .

Syntax

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.

Output

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.

Permissions

You must have the bigquery.models.create and bigquery.models.getData Identity and Access Management (IAM) permissions in order to run ML.FEATURE_INFO .

Limitations

ML.FEATURE_INFO doesn't support imported TensorFlow models .

Example

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

• For information about feature preprocessing, see Feature preprocessing overview .