evalml.pipelines.components.Imputer.__init__

Imputer.__init__(categorical_impute_strategy='most_frequent', categorical_fill_value=None, numeric_impute_strategy='mean', numeric_fill_value=None, random_seed=0, **kwargs)[source]

Initalizes an transformer that imputes missing data according to the specified imputation strategy.”

Parameters

  • categorical_impute_strategy (string) – Impute strategy to use for string, object, boolean, categorical dtypes. Valid values include “most_frequent” and “constant”.

  • numeric_impute_strategy (string) – Impute strategy to use for numeric columns. Valid values include “mean”, “median”, “most_frequent”, and “constant”.

  • categorical_fill_value (string) – When categorical_impute_strategy == “constant”, fill_value is used to replace missing data. The default value of None will fill with the string “missing_value”.

  • numeric_fill_value (int, float) – When numeric_impute_strategy == “constant”, fill_value is used to replace missing data. The default value of None will fill with 0.

  • random_seed (int) – Seed for the random number generator. Defaults to 0.

evalml.pipelines.components.Imputer evalml.pipelines.components.Imputer.clone