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Iterative imputation

In the previous exercise, you derived mean imputations for missing values of loan_data. However, in a machine learning interview, you will probably be asked about more dynamic imputation techniques that rely on other features in the dataset.

In this exercise, you'll practice a machine-learning based approach by imputing missing values as a function of remaining features using IterativeImputer() from sklearn.impute. This is a multivariate imputer that estimates each feature from all of the others in a 'round-robin' fashion.

Note that this function is considered experimental, so please read the documentation for more information.

You're at the same place in the Pipeline:

Machine learning pipeline

This exercise is part of the course

Practicing Machine Learning Interview Questions in Python

View Course

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Explicitly require this experimental feature
from sklearn.experimental import enable_iterative_imputer
# Now you can import normally from sklearn.impute
from sklearn.impute import IterativeImputer

# Subset numeric features: numeric_cols
numeric_cols = ____.____(____=[____.____])
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