The ArbitraryOutlierCapper() caps the maximum or minimum values of a variable at an arbitrary value indicated by the user. The maximum or minimum values should be entered in a dictionary with the form {feature:capping value}.

Let’s look at this in an example. First we load the Titanic dataset, and separate it into a train and a test set:

from sklearn.model_selection import train_test_split
from feature_engine.datasets import load_titanic
from feature_engine.outliers import ArbitraryOutlierCapper

X, y = load_titanic(

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=0,


We see the resulting data below:

      pclass     sex        age  sibsp  parch     fare    cabin embarked
501        2  female  13.000000      0      1  19.5000  Missing        S
588        2  female   4.000000      1      1  23.0000  Missing        S
402        2  female  30.000000      1      0  13.8583  Missing        C
1193       3    male  29.881135      0      0   7.7250  Missing        Q
686        3  female  22.000000      0      0   7.7250  Missing        Q

Now, we set up the ArbitraryOutlierCapper() indicating that we want to cap the variable ‘age’ at 50 and the variable ‘Fare’ at 200. We do not want to cap these variables on the left side of their distribution.

capper = ArbitraryOutlierCapper(
    max_capping_dict={'age': 50, 'fare': 200},

With fit() the transformer does not learn any parameter. It just reassigns the entered dictionary to the attribute that will be used in the transformation:

{'age': 50, 'fare': 200}

Now, we can go ahead and cap the variables:

train_t = capper.transform(X_train)
test_t = capper.transform(X_test)

If we now check the maximum values in the transformed data, they should be those entered in the dictionary:

train_t[['fare', 'age']].max()
fare    200.0
age      50.0
dtype: float64

Additional resources#

You can find more details about the ArbitraryOutlierCapper() functionality in the following notebook:

For more details about this and other feature engineering methods check out these resources:


Feature Engineering for Machine Learning#

Or read our book:


Python Feature Engineering Cookbook#

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