Automatic feature preprocessing

BigQuery ML performs automatic preprocessing during training by using the CREATE MODEL statement. Automatic preprocessing consists of missing value imputation and feature transformations.

For information about feature preprocessing support in BigQuery ML, 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.

Missing data imputation

In statistics, imputation is used to replace missing data with substituted values. When you train a model in BigQuery ML, NULL values are treated as missing data. When you predict outcomes in BigQuery ML, missing values can occur when BigQuery ML encounters a NULL value or a previously unseen value. BigQuery ML handles missing data differently, based on the type of data in the column.

Column type Imputation method
Numeric In both training and prediction, NULL values in numeric columns are replaced with the mean value of the given column, as calculated by the feature column in the original input data.
One-hot/Multi-hot encoded In both training and prediction, NULL values in the encoded columns are mapped to an additional category that is added to the data. Previously unseen data is assigned a weight of 0 during prediction.
TIMESTAMP TIMESTAMP columns use a mixture of imputation methods from both standardized and one-hot encoded columns. For the generated Unix time column, BigQuery ML replaces values with the mean Unix time across the original columns. For other generated values, BigQuery ML assigns them to the respective NULL category for each extracted feature.
STRUCT In both training and prediction, each field of the STRUCT is imputed according to its type.

Feature transformations

By default, BigQuery ML transforms input features as follows:

Input data type Transformation method Details
Standardization For most models, BigQuery ML standardizes and centers numerical columns at zero before passing it into training. The exceptions are boosted tree and random forest models, for which no standardization occurs, and k-means models, where the STANDARDIZE_FEATURES option controls whether numerical features are standardized.
One-hot encoded For all non-numerical, non-array columns other than TIMESTAMP, BigQuery ML performs a one-hot encoding transformation for all models other than boosted tree and random forest models. This transformation generates a separate feature for each unique value in the column. Label encoding transformation is applied to train boosted tree and random forest models to convert each unique value into a numerical value.
ARRAY Multi-hot encoded For all non-numerical ARRAY columns, BigQuery ML performs a multi-hot encoding transformation. This transformation generates a separate feature for each unique element in the ARRAY.
TIMESTAMP Timestamp transformation When a linear or logistic regression model encounters a TIMESTAMP column, it extracts a set of components from the TIMESTAMP and performs a mix of standardization and one-hot encoding on the extracted components. For the Unix time in seconds component, BigQuery ML uses standardization. For all other components, it uses one-hot encoding.

For more information, see the timestamp feature transformation table below.
STRUCT Struct expansion When BigQuery ML encounters a STRUCT column, it expands the fields inside the STRUCT to create a single column. It requires all fields of STRUCT to be named. Nested STRUCTs are not allowed. The column names after expansion are in the format of {struct_name}_{field_name}.
ARRAY of STRUCT No transformation
ARRAY of NUMERIC No transformation

TIMESTAMP feature transformation

The following table shows the components extracted from TIMESTAMP columns and the corresponding transformation method.

TIMESTAMP component processed_input result Transformation method
Unix time in seconds [COLUMN_NAME] Standardization
Day of month _TS_DOM_[COLUMN_NAME] One-hot encoding
Day of week _TS_DOW_[COLUMN_NAME] One-hot encoding
Month of year _TS_MOY_[COLUMN_NAME] One-hot encoding
Hour of day _TS_HOD_[COLUMN_NAME] One-hot encoding
Minute of hour _TS_MOH_[COLUMN_NAME] One-hot encoding
Week of year (weeks begin on Sunday) _TS_WOY_[COLUMN_NAME] One-hot encoding
Year _TS_YEAR_[COLUMN_NAME] One-hot encoding

Category feature encoding

For features that are one-hot encoded, you can specify a different default encoding method by using the model option CATEGORY_ENCODING_METHOD. For generalized linear models (GLM) models, you can set CATEGORY_ENCODING_METHOD to one of the following values:

One-hot encoding

One-hot encoding maps each category that a feature has to its own binary feature, where 0 represents the absence of the feature and 1 represents the presence (known as a dummy variable). This mapping creates N new feature columns, where N is the number of unique categories for the feature across the training table.

For example, suppose your training table has a feature column that's called fruit with the categories Apple, Banana, and Cranberry, such as the following:

Row fruit
1 Apple
2 Banana
3 Cranberry

In this case, the CATEGORY_ENCODING_METHOD='ONE_HOT_ENCODING' option transforms the table to the following internal representation:

Row fruit_Apple fruit_Banana fruit_Cranberry
1 1 0 0
2 0 1 0
3 0 0 1

One-hot encoding is supported by linear and logistic regression and boosted tree models.

Dummy encoding

Dummy encoding is similar to one-hot encoding, where a categorical feature is transformed into a set of placeholder variables. Dummy encoding uses N-1 placeholder variables instead of N placeholder variables to represent N categories for a feature. For example, if you set CATEGORY_ENCODING_METHOD to 'DUMMY_ENCODING' for the same fruit feature column shown in the preceding one-hot encoding example, then the table is transformed to the following internal representation:

Row fruit_Apple fruit_Banana
1 1 0
2 0 1
3 0 0

The category with the most occurrences in the training dataset is dropped. When multiple categories have the most occurrences, a random category within that set is dropped.

The final set of weights from ML.WEIGHTS still includes the dropped category, but its weight is always 0.0. For ML.ADVANCED_WEIGHTS, the standard error and p-value for the dropped variable is NaN.

If warm_start is used on a model that was initially trained with 'DUMMY_ENCODING', the same placeholder variable is dropped from the first training run. Models cannot change encoding methods between training runs.

Dummy encoding is supported by linear and logistic regression models.

Label encoding

Label encoding transforms the value of a categorical feature to an INT64 value in [0, <number of categories>].

For example, if you had a book dataset like the following:

Title Genre
Book 1 Fantasy
Book 2 Cooking
Book 3 History
Book 4 Cooking

The label encoded values might look similar to the following:

Title Genre (text) Genre (numeric)
Book 1 Fantasy 1
Book 2 Cooking 2
Book 3 History 3
Book 4 Cooking 2

The encoding vocabulary is sorted alphabetically. NULL values and categories that aren't in the vocabulary are encoded to 0.

Label encoding is supported by boosted tree models.

Target encoding

Target encoding replaces the categorical feature value with the probability of the target for classification models, or with the expected value of the target for regression models.

Features that have been target encoded might look similar to the following example:

# Classification model
| original value         | target encoded value |
| (category_1, target_1) |     0.5              |
| (category_1, target_2) |     0.5              |
| (category_2, target_1) |     0.0              |

# Regression model
| original value         | target encoded value |
| (category_1, 2)        |     2.5              |
| (category_1, 3)        |     2.5              |
| (category_2, 1)        |     1.5              |
| (category_2, 2)        |     1.5              |

Target encoding is supported by boosted tree models.