class feature_engine.encoding.WoEEncoder(variables=None, ignore_format=False, unseen='ignore', fill_value=None)[source]#

The WoEEncoder() replaces categories by the weight of evidence (WoE). The WoE was used primarily in the financial sector to create credit risk scorecards.

The encoder will encode only categorical variables by default (type ‘object’ or ‘categorical’). You can pass a list of variables to encode. Alternatively, the encoder will find and encode all categorical variables (type ‘object’ or ‘categorical’).

With ignore_format=True you have the option to encode numerical variables as well. The procedure is identical, you can either enter the list of variables to encode, or the transformer will automatically select all variables.

The encoder first maps the categories to the weight of evidence for each variable (fit). The encoder then transforms the categories into the mapped numbers (transform).

This categorical encoding is exclusive for binary classification.


The log(0) is not defined and the division by 0 is not defined. Thus, if any of the terms in the WoE equation are 0 for a given category, the encoder will return an error. If this happens, try grouping less frequent categories. Alternatively, you can now add a fill_value (see parameter below).

More details in the User Guide.

variables: list, default=None

The list of categorical variables that will be encoded. If None, the encoder will find and transform all variables of type object or categorical by default. You can also make the transformer accept numerical variables, see the parameter ignore_format.

ignore_format: bool, default=False

This transformer operates only on variables of type object or categorical. To override this behaviour and allow the transformer to transform numerical variables as well, set to True.

If ignore_format is False, the encoder will automatically select variables of type object or categorical, or check that the variables entered by the user are of type object or categorical. If True, the encoder will select all variables or accept all variables entered by the user, including those cast as numeric.

In short, set to True when you want to encode numerical variables.

unseen: string, default=’ignore’

Indicates what to do when categories not present in the train set are encountered during transform. If 'raise', then unseen categories will raise an error. If 'ignore', then unseen categories will be encoded as NaN and a warning will be raised instead.

fill_value: int, float, default=None

When the numerator or denominator of the WoE calculation are zero, the WoE calculation is not possible. If fill_value is None (recommended), an error will be raised in those cases. Alternatively, fill_value will be used in place of denominators or numerators that equal zero.


Dictionary with the WoE per variable.


The group of variables that will be transformed.


List with the names of features seen during fit.


The number of features in the train set used in fit.

See also



For details on the calculation of the weight of evidence visit: https://www.listendata.com/2015/03/weight-of-evidence-woe-and-information.html

NAN are introduced when encoding categories that were not present in the training dataset. If this happens, try grouping infrequent categories using the RareLabelEncoder().

There is a similar implementation in the the open-source package Category encoders


>>> import pandas as pd
>>> from feature_engine.encoding import WoEEncoder
>>> X = pd.DataFrame(dict(x1 = [1,2,3,4,5], x2 = ["b", "b", "b", "a", "a"]))
>>> y = pd.Series([0,1,1,1,0])
>>> woe = WoEEncoder()
>>> woe.fit(X, y)
>>> woe.transform(X)
   x1        x2
0   1  0.287682
1   2  0.287682
2   3  0.287682
3   4 -0.405465
4   5 -0.405465



Learn the WoE per category, per variable.


Encode the categories to numbers.


Fit to data, then transform it.


Get output feature names for transformation.


Get parameters for this estimator.


Set the parameters of this estimator.


Convert the data back to the original representation.

fit(X, y)[source]#

Learn the WoE.

X: pandas dataframe of shape = [n_samples, n_features]

The training input samples. Can be the entire dataframe, not just the categorical variables.

y: pandas series.

Target, must be binary.

fit_transform(X, y=None, **fit_params)[source]#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).


Additional fit parameters.

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.


Get output feature names for transformation. In other words, returns the variable names of transformed dataframe.

input_featuresarray or list, default=None

This parameter exits only for compatibility with the Scikit-learn pipeline.

  • If None, then feature_names_in_ is used as feature names in.

  • If an array or list, then input_features must match feature_names_in_.

feature_names_out: list

Transformed feature names.


List[Union[str, int]] ..


Get parameters for this estimator.

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.


Parameter names mapped to their values.


Convert the encoded variable back to the original values.

X: pandas dataframe of shape = [n_samples, n_features].

The transformed dataframe.

X_tr: pandas dataframe of shape = [n_samples, n_features].

The un-transformed dataframe, with the categorical variables containing the original values.


DataFrame ..


Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.


Estimator parameters.

selfestimator instance

Estimator instance.


Replace categories with the learned parameters.

X: pandas dataframe of shape = [n_samples, n_features].

The dataset to transform.

X_new: pandas dataframe of shape = [n_samples, n_features].

The dataframe containing the categories replaced by numbers.


DataFrame ..