On many occasions, you have features in dataset that you do not necessarily want to remove but want to ignore for training a machine learning model. A good example would be a clustering problem where you want to ignore certain features during cluster creation but later you need those columns for analysis of cluster labels. In such cases, you can use the ignore_features parameter within the setup to ignore such features.
In the example below, we will perform a clustering experiment and we want to ignore 'Country Name' and 'Indicator Name'.
from pycaret.clustering import * clu1 = setup(data, ignore_features = ['Country Name', 'Indicator Name'])
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