The ML.IMPUTER function

This document describes the ML.IMPUTER function, which lets you replace NULL values in a string or numerical expression. You can replace NULL values with the most frequently used value for string expressions, or the mean or median value for numerical expressions.

When used in the TRANSFORM clause , the values calculated during training for mean, median, and most frequently used value are automatically used in prediction.

Syntax

ML.IMPUTER(expression, strategy) OVER()

Arguments

ML.IMPUTER takes the following arguments:

• expression : the numerical or STRING expression to impute.
• strategy : a STRING value that specifies how to replace NULL values. Valid values are as follows:
  • mean : the mean of expression . You can only use this value with numerical expressions.
  • median : the median of expression . You can only use this value with numerical expressions.
  • most_frequent : the most frequent value in expression .

Output

ML.IMPUTER returns a FLOAT64 (for numerical expressions) or STRING (for string expressions) value that contains the replacement for the NULL value.

Examples

Example 1

The following example imputes numerical expressions:

 SELECT 
  
 f 
 , 
  
 ML 
 . 
 IMPUTER 
 ( 
 f 
 , 
  
 'mean' 
 ) 
  
 OVER 
  
 () 
  
 AS 
  
 output 
 FROM 
  
 UNNEST 
 ([ 
 NULL 
 , 
  
 - 
 3 
 , 
  
 - 
 3 
 , 
  
 - 
 3 
 , 
  
 1 
 , 
  
 2 
 , 
  
 3 
 , 
  
 4 
 , 
  
 5 
 ]) 
  
 AS 
  
 f 
 ORDER 
  
 BY 
  
 f 
 ; 

The output looks similar to the following:

+------+--------+
|  f   | output |
+------+--------+
| NULL |   0.75 |
|   -3 |   -3.0 |
|   -3 |   -3.0 |
|   -3 |   -3.0 |
|    1 |    1.0 |
|    2 |    2.0 |
|    3 |    3.0 |
|    4 |    4.0 |
|    5 |    5.0 |
+------+--------+

Example 2

The following example imputes string expressions:

 SELECT 
  
 f 
 , 
  
 ML 
 . 
 IMPUTER 
 ( 
 f 
 , 
  
 'most_frequent' 
 ) 
  
 OVER 
  
 () 
  
 AS 
  
 output 
 FROM 
  
 UNNEST 
 ([ 
 NULL 
 , 
  
 NULL 
 , 
  
 NULL 
 , 
  
 NULL 
 , 
  
 'a' 
 , 
  
 'a' 
 , 
  
 'b' 
 , 
  
 'b' 
 , 
  
 'c' 
 , 
  
 'c' 
 , 
  
 'c' 
 ]) 
  
 AS 
  
 f 
 ORDER 
  
 BY 
  
 f 
 ; 

The output looks similar to the following:

+------+--------+
|  f   | output |
+------+--------+
| NULL | c      |
| NULL | c      |
| NULL | c      |
| NULL | c      |
| a    | a      |
| a    | a      |
| b    | b      |
| b    | b      |
| c    | c      |
| c    | c      |
| c    | c      |
+------+--------+

What's next

• For information about feature preprocessing, see Feature preprocessing overview .
• For information about the supported SQL statements and functions for each model type, see End-to-end user journey for each model .