Non-missing values in the target column will be used to train a prediction model (a Catboost regressor or classifier), which then predicts (imputes) the missing values. Only simple numerical or categorical input data can be imputed.

Usage

The following example shows how the step can be used in a recipe.

Inputs & Outputs

The following are the inputs expected by the step and the outputs it produces. These are generally columns (ds.first_name), datasets (ds or ds[["first_name", "last_name"]]) or models (referenced by name e.g. "churn-clf").

Configuration

The following parameters can be used to configure the behaviour of the step by including them in a json object as the last “input” to the step, i.e. step(..., {"param": "value", ...}) -> (output).