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 data X
fit_transform	Fits imputer on data X then imputes missing values in X
load	Loads component at file path
save	Saves component at file path
transform	Transforms data X by imputing missing values

evalml.pipelines.components.OneHotEncoder.transform evalml.pipelines.components.PerColumnImputer.__init__