Kidney disease case study II: Feature Union

Having separately imputed numeric as well as categorical columns, your task is now to use scikit-learn's FeatureUnion to concatenate their results, which are contained in two separate transformer objects - numeric_imputation_mapper, and categorical_imputation_mapper, respectively.

You may have already encountered FeatureUnion in Machine Learning with the Experts: School Budgets. Just like with pipelines, you have to pass it a list of (string, transformer) tuples, where the first half of each tuple is the name of the transformer.

Deze oefening maakt deel uit van de cursus

Extreme Gradient Boosting with XGBoost

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Oefeninstructies

Import FeatureUnion from sklearn.pipeline.
Combine the results of numeric_imputation_mapper and categorical_imputation_mapper using FeatureUnion(), with the names "num_mapper" and "cat_mapper" respectively.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import FeatureUnion
from sklearn.pipeline import FeatureUnion

# Combine the numeric and categorical transformations
numeric_categorical_union = ____([
                                          ("____", ____),
                                          ("____", ____)
                                         ])

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