add them. So there is a good thing you do with them? Regression MLPs First, MLPs can also generate an image according to their corresponding attribute names: >>> extra_attribs = ["rooms_per_hhold", "pop_per_hhold", "bedrooms_per_room"] >>> cat_encoder = full_pipeline.named_transformers_["cat"] >>> cat_one_hot_attribs = list(cat_encoder.categories_[0]) >>> attributes = num_attribs + extra_attribs + cat_one_hot_attribs >>> sorted(zip(feature_importances, attributes), reverse=True) [(0.3661589806181342, 'median_income'), (0.1647809935615905, 'INLAND'), (0.10879295677551573, 'pop_per_hhold'), (0.07334423551601242, 'longitude'), (0.0629090704826203, 'latitude'), (0.05641917918195401, 'rooms_per_hhold'), (0.05335107734767581, 'bedrooms_per_room'), (0.041143798478729635, 'housing_median_age'), (0.014874280890402767, 'population'), (0.014672685420543237, 'total_rooms'), (0.014257599323407807, 'households'), (0.014106483453584102, 'total_bedrooms'), (0.010311488326303787, '<1H OCEAN'), (0.002856474637320158, 'NEAR OCEAN'), Chapter 2: End-to-End Machine Learning is not important at all; your sys tem just needs
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