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.

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

Attributes:

feature_names_out_

Methods

`fit`(X[, y])	Fits the encoder according to X and y.
`fit_transform`(X[, y])	Encoders that utilize the target must make sure that the training data are transformed with:
`get_feature_names_in`()	Returns the names of all input columns present when fitting.
`get_feature_names_out`()	Returns the names of all transformed / added columns.
`get_params`([deep])	Get parameters for this estimator.
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X[, y, override_return_df])	Perform the transformation to new categorical data.

get_feature_names

Parameters:

verbose: int: integer indicating verbosity of output. 0 for none.
cols: list: a list of columns to encode, if None, all string and categorical columns will be encoded.
drop_invariant: bool: boolean for whether or not to drop columns with 0 variance.
return_df: bool: boolean for whether to return a pandas DataFrame from transform and inverse transform (otherwise it will be a numpy array).
handle_missing: str: how to handle missing values at fit time. Options are ‘error’, ‘return_nan’, and ‘value’. Default ‘value’, which treat NaNs as a countable category at fit time.
handle_unknown: str, int or dict of {columnoption, …}.: how to handle unknown labels at transform time. Options are ‘error’ ‘return_nan’, ‘value’ and int. Defaults to None which uses NaN behaviour specified at fit time. Passing an int will fill with this int value.
kwargs: dict.: additional encoder specific parameters like regularisation.

Attributes:

feature_names_out_

Methods

`fit`(X[, y])	Fits the encoder according to X and y.
`fit_transform`(X[, y])	Encoders that utilize the target must make sure that the training data are transformed with:
`get_feature_names_in`()	Returns the names of all input columns present when fitting.
`get_feature_names_out`()	Returns the names of all transformed / added columns.
`get_params`([deep])	Get parameters for this estimator.
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X[, y, override_return_df])	Perform the transformation to new categorical data.

get_feature_names

fit(X, y=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, y=None, **fit_params)

Encoders that utilize the target must make sure that the training data are transformed with:: transform(X, y)
and not with:: transform(X)

get_feature_names_in() → List[str]: Returns the names of all input columns present when fitting. These columns are necessary for the transform step.

get_feature_names_out() → List[str]

Returns the names of all transformed / added columns.

Returns:

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

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”}, default=None

Configure output of transform and fit_transform.

“default”: Default output format of a transformer
“pandas”: DataFrame output
None: Transform configuration is unchanged

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.

transform(X, y=None, override_return_df=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.