evalml.pipelines.components.PerColumnImputer

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

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 and random state.

describe

Describe a component and its parameters

fit

Fits imputers on input data

fit_transform

Fits on X and transforms X

load

Loads component at file path

save

Saves component at file path

transform

Transforms input data by imputing missing values.

Class Inheritance

Inheritance diagram of PerColumnImputer