M-estimate

class category_encoders.m_estimate.MEstimateEncoder(verbose=0, cols=None, drop_invariant=False, return_df=True, handle_unknown='value', handle_missing='value', random_state=None, randomized=False, sigma=0.05, m=1.0)[source]

M-probability estimate of likelihood.

Supported targets: binomial and continuous. For polynomial target support, see PolynomialWrapper.

This is a simplified version of target encoder, which goes under names like m-probability estimate or additive smoothing with known incidence rates. In comparison to target encoder, m-probability estimate has only one tunable parameter (m), while target encoder has two tunable parameters (min_samples_leaf and smoothing).

Parameters:
verbose: int

integer indicating verbosity of the output. 0 for none.

cols: list

a list of columns to encode, if None, all string columns will be encoded.

drop_invariant: bool

boolean for whether or not to drop encoded columns with 0 variance.

return_df: bool

boolean for whether to return a pandas DataFrame from transform (otherwise it will be a numpy array).

handle_missing: str

options are ‘return_nan’, ‘error’ and ‘value’, defaults to ‘value’, which returns the prior probability.

handle_unknown: str

options are ‘return_nan’, ‘error’ and ‘value’, defaults to ‘value’, which returns the prior probability.

randomized: bool,

adds normal (Gaussian) distribution noise into training data in order to decrease overfitting (testing data are untouched).

sigma: float

standard deviation (spread or “width”) of the normal distribution.

m: float

this is the “m” in the m-probability estimate. Higher value of m results into stronger shrinking. M is non-negative.

Methods

fit(X[, y])

Fits the encoder according to X and y.

fit_transform(X[, y])

Fit and transform using the target information.

get_feature_names()

Deprecated method to get feature names.

get_feature_names_in()

Get the names of all input columns present when fitting.

get_feature_names_out([input_features])

Get the names of all transformed / added columns.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_output(*[, transform])

Set output container.

set_params(**params)

Set the parameters of this estimator.

set_transform_request(*[, override_return_df])

Configure whether metadata should be requested to be passed to the transform method.

transform(X[, y, override_return_df])

Perform the transformation to new categorical data.

References

[1]

A Preprocessing Scheme for High-Cardinality Categorical Attributes in Classification

and Prediction Problems, equation 7, from https://dl.acm.org/citation.cfm?id=507538

[2]

On estimating probabilities in tree pruning, equation 1, from

https://link.springer.com/chapter/10.1007/BFb0017010

[3]

Additive smoothing, from

https://en.wikipedia.org/wiki/Additive_smoothing#Generalized_to_the_case_of_known_incidence_rates

fit(X: ndarray | DataFrame | list | generic | csr_matrix, y: list | Series | ndarray | tuple | DataFrame | None = None, **kwargs)

Fits the encoder according to X and y.

Parameters:
Xarray-like, shape = [n_samples, n_features]

Training vectors, where n_samples is the number of samples and n_features is the number of features.

yarray-like, shape = [n_samples]

Target values.

Returns:
selfencoder

Returns self.

fit_transform(X: ndarray | DataFrame | list | generic | csr_matrix, y: list | Series | ndarray | tuple | DataFrame | None = None, **fit_params)

Fit and transform using the target information.

This also uses the target for transforming, not only for training.

get_feature_names() → ndarray

Deprecated method to get feature names. Use get_feature_names_out instead.

get_feature_names_in() → ndarray

Get the names of all input columns present when fitting.

These columns are necessary for the transform step.

get_feature_names_out(input_features=None) → ndarray

Get the names of all transformed / added columns.

Note that in sklearn the get_feature_names_out function takes the feature_names_in as an argument and determines the output feature names using the input. A fit is usually not necessary and if so a NotFittedError is raised. We just require a fit all the time and return the fitted output columns.

Returns:
feature_names: np.ndarray

A numpy array with all feature names transformed or added. Note: potentially dropped features (because the feature is constant/invariant) are not included!

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

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.

set_output(*, transform=None)

Set output container.

See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.

Parameters:
transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

  • “default”: Default output format of a transformer

  • “pandas”: DataFrame output

  • “polars”: Polars output

  • None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:
selfestimator instance

Estimator instance.

set_params(**params)

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.

set_transform_request(*, override_return_df: bool | None | str = '$UNCHANGED$') → MEstimateEncoder

Configure whether metadata should be requested to be passed to the transform method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to transform.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:
override_return_dfstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for override_return_df parameter in transform.

Returns:
selfobject

The updated object.

transform(X: ndarray | DataFrame | list | generic | csr_matrix, y: list | Series | ndarray | tuple | DataFrame | None = None, override_return_df: bool = False)

Perform the transformation to new categorical data.

Some encoders behave differently on whether y is given or not. This is mainly due to regularisation in order to avoid overfitting. On training data transform should be called with y, on test data without.

Parameters:
Xarray-like, shape = [n_samples, n_features]
yarray-like, shape = [n_samples] or None
override_return_dfbool

override self.return_df to force to return a data frame

Returns:
parray or DataFrame, shape = [n_samples, n_features_out]

Transformed values with encoding applied.