Implementation:Scikit learn Scikit learn EnableIterativeImputer

Knowledge Sources	Scikit_learn Scikit-learn Docs
Domains	Machine Learning, Data Preprocessing
Last Updated	2026-02-08 15:00 GMT

Overview

Concrete tool for enabling the experimental IterativeImputer in scikit-learn's impute module.

Description

Importing this module dynamically sets IterativeImputer as an attribute of the sklearn.impute module. The IterativeImputer is an experimental estimator that models each feature with missing values as a function of other features and uses that estimate for imputation, performing multiple rounds of imputation.

Usage

Import this module before importing IterativeImputer from sklearn.impute. This is required because the estimator is experimental and its API and results may change without deprecation cycles.

Code Reference

Source Location

• Repository: scikit-learn
• File: sklearn/experimental/enable_iterative_imputer.py

Signature

# Module-level side effects:
from sklearn.impute._iterative import IterativeImputer
setattr(impute, "IterativeImputer", IterativeImputer)

Import

from sklearn.experimental import enable_iterative_imputer  # noqa
from sklearn.impute import IterativeImputer

I/O Contract

Inputs

Name	Type	Required	Description
(none)	N/A	N/A	This is a side-effect module; importing it enables the experimental IterativeImputer

Outputs

Name	Type	Description
IterativeImputer	class	Experimental iterative imputation estimator added to sklearn.impute

Usage Examples

Basic Usage

import numpy as np
from sklearn.experimental import enable_iterative_imputer  # noqa
from sklearn.impute import IterativeImputer

X = np.array([[1, 2], [np.nan, 3], [7, 6]])
imp = IterativeImputer(max_iter=10, random_state=0)
X_imputed = imp.fit_transform(X)
print(X_imputed)

Related Pages

• Principle:Scikit_learn_Scikit_learn_Missing_Data_Imputation