Winsorizer#

The Winsorizer() caps maximum and/or minimum values of a variable at automatically determined values. The minimum and maximum values can be calculated in 1 of 3 different ways:

Gaussian limits:

  • right tail: mean + 3* std

  • left tail: mean - 3* std

IQR limits:

  • right tail: 75th quantile + 3* IQR

  • left tail: 25th quantile - 3* IQR

where IQR is the inter-quartile range: 75th quantile - 25th quantile.

MAD limits:

  • right tail: median + 3* MAD

  • left tail: median - 3* MAD

where MAD is the median absolute deviation from the median.

percentiles or quantiles:

  • right tail: 95th percentile

  • left tail: 5th percentile

Example

Let’s cap some outliers in the Titanic Dataset. First, let’s load the data and separate it into train and test:

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

X, y = load_titanic(
    return_X_y_frame=True,
    predictors_only=True,
    handle_missing=True,
)


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

print(X_train.head())

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 will set the Winsorizer() to cap outliers at the right side of the distribution only (param tail). We want the maximum values to be determined using the mean value of the variable (param capping_method) plus 3 times the standard deviation (param fold). And we only want to cap outliers in 2 variables, which we indicate in a list.

capper = Winsorizer(capping_method='gaussian',
                    tail='right',
                    fold=3,
                    variables=['age', 'fare'])

capper.fit(X_train)

With fit(), the Winsorizer() finds the values at which it should cap the variables. These values are stored in its attribute:

capper.right_tail_caps_

{'age': 67.73951212364803, 'fare': 174.70395336846678}

We can now go ahead and censor the outliers:

# transform the data
train_t = capper.transform(X_train)
test_t = capper.transform(X_test)

If we evaluate now the maximum of the variables in the transformed datasets, they should coincide with the values observed in the attribute right_tail_caps_:

train_t[['fare', 'age']].max()

fare    174.703953
age      67.739512
dtype: float64

More details#

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

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