These preprocessing steps may be specified on a per-column basis to the[train()](/user_guides/training/overview/) function. By default, PostgresML does minimal preprocessing on training data, and will raise an error during analysis if NULL values are encountered without a preprocessor. All types other than`TEXT` are treated as quantitative variables and cast to floating point representations before passing them to the underlying algorithm implementations.

```postgresql title="pgml.train()"

!!! note

TEXT is used in this document to also refer to VARCHAR and CHAR(N) types.

A model that has been trained with preprocessors should use a Postgres tuple for prediction, rather than a`FLOAT4[]`. Tuples may contain multiple different types (like`TEXT` and`BIGINT`), while an ARRAY may only contain a single type. You can use parenthesis around values to create a Postgres tuple.

```postgresql title="pgml.predict()"

Encoding categorical variables is an O(N log(M)) where N is the number of rows, and M is the number of distinct categories.

-Training:

-Training Overview:user_guides/training/overview.md

-Algorithm Selection:user_guides/training/algorithm_selection.md

-Preprocessing Data:user_guides/training/preprocessing.md

-Hyperparameter Search:user_guides/training/hyperparameter_search.md

-Joint Optimization:user_guides/training/joint_optimization.md

-Predictions: