MeanEncoder

class feature_engine.encoding.MeanEncoder(variables=None, ignore_format=False, errors='ignore')[source]

The MeanEncoder() replaces categories by the mean value of the target for each category.

For example in the variable colour, if the mean of the target for blue, red and grey is 0.5, 0.8 and 0.1 respectively, blue is replaced by 0.5, red by 0.8 and grey by 0.1.

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 numbers for each variable (fit). The encoder then replaces the categories with those numbers (transform).

More details in the User Guide.

Parameters
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 next parameter.

ignore_format: bool, default=False

Whether the format in which the categorical variables are cast should be ignored. If 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.

errors: string, default=’ignore’

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

Attributes
encoder_dict_:

Dictionary with the target mean value per category per variable.

variables_:

The group of variables that will be transformed.

n_features_in_:

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

See also

feature_engine.encoding.RareLabelEncoder
category_encoders.target_encoder.TargetEncoder
category_encoders.m_estimate.MEstimateEncoder

Notes

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

Check also the related transformers in the the open-source package Category encoders

References

1

Micci-Barreca D. “A Preprocessing Scheme for High-Cardinality Categorical Attributes in Classification and Prediction Problems”. ACM SIGKDD Explorations Newsletter, 2001. https://dl.acm.org/citation.cfm?id=507538

Methods

fit:

Learn the target mean value per category, per variable.

transform:

Encode the categories to numbers.

fit_transform:

Fit to the data, then transform it.

inverse_transform:

Encode the numbers into the original categories.

fit(X, y)[source]

Learn the mean value of the target for each category of the variable.

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

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

y: pandas series

The target.

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.

Parameters
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).

**fit_paramsdict

Additional fit parameters.

Returns
X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters
deepbool, default=True

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

Returns
paramsdict

Parameter names mapped to their values.

inverse_transform(X)[source]

Convert the encoded variable back to the original values.

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

The transformed dataframe.

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

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

rtype

DataFrame ..

set_params(**params)[source]

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.

Parameters
**paramsdict

Estimator parameters.

Returns
selfestimator instance

Estimator instance.

transform(X)[source]

Replace categories with the learned parameters.

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

The dataset to transform.

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

The dataframe containing the categories replaced by numbers.

rtype

DataFrame ..