evalml.pipelines.components.PerColumnImputer

class evalml.pipelines.components.PerColumnImputer(impute_strategies=None, default_impute_strategy='most_frequent', random_state=0, **kwargs)

Imputes missing data according to a specified imputation strategy per column

name = 'Per Column Imputer'

model_family = 'none'

hyperparameter_ranges = {}

default_parameters = {'default_impute_strategy': 'most_frequent', 'impute_strategies': None}

Instance attributes

needs_fitting
parameters	Returns the parameters which were used to initialize the component

Methods:

__init__	Initializes a transformer that imputes missing data according to the specified imputation strategy per column.”
clone	Constructs a new component with the same parameters
describe	Describe a component and its parameters
fit	Fits imputers on input data
fit_transform	Fits imputer and imputes missing values in input data.
load	Loads component at file path
save	Saves component at file path
transform	Transforms input data by imputing missing values.

Class Inheritance

evalml.pipelines.components.TargetEncoder.transform evalml.pipelines.components.PerColumnImputer.__init__