Handle Outliers
Feature-engine, a Python library for feature engineering
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Published Oct 3 2025
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Feature EngineeringFeature-engineMachine LearningPandasPythonscikit-learnTransformers
These techniques aim to cap outliers based on a calculation or an arbitrary value. In addition, you may drop the outliers from the dataset.
Winsorizer
It caps the outliers as a continuous variable's maximum and/or minimum values. It calculates the capping values using specific methods.
Parameters:
capping_method: method used to determine what is considered an outlier, possible values are: 'gaussian', 'iqr', 'mad' or 'quantiles'tail: whether to look for outliers on the right, left or both tails of the distribution, possible values are: 'left, 'right or 'both'fold: The multiplication factor, what is multiplied is determined by the capping_method. You can use 'auto' to use the default factor for the selected capping_method.
wiqr = Winsorizer(capping_method='iqr', fold=1.5, tail='both', variables=['bmi', 'charges'])
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You can view the left and right tail caps by looking at wiqr.right_tail_caps_ or wiqr.left_tail_caps_ respectively.
Arbitrary Outlier Capper
It caps a variable's maximum or minimum values at an arbitrary value indicated by the user.
Parameters:
max_capping_dict: A dictionary of the column names and the max values.min_capping_dict: A dictionary of the column names and the min values.
aoc = ArbitraryOutlierCapper(max_capping_dict={'charges':20000 , 'bmi':40})
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Outlier Trimmer
It removes observations with outliers from the data.
Parameters:
capping_method: method used to determine what is considered an outlier, possible values are: 'gaussian', 'iqr', 'mad' or 'quantiles'tail: whether to look for outliers on the right, left or both tails of the distribution, possible values are: 'left, 'right or 'both'fold: The multiplication factor, what is multiplied is determined by the capping_method. You can use 'auto' to use the default factor for the selected capping_method.
ot = OutlierTrimmer(capping_method='iqr', fold=1.5, tail='both', variables=['bmi', 'charges'])
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