Summary Encoder

class category_encoders.quantile_encoder.SummaryEncoder(verbose=0, cols=None, drop_invariant=False, return_df=True, handle_missing='value', handle_unknown='value', quantiles=(0.25, 0.75), m=1.0)[source]

Summary Encoding for categorical features.

It’s an encoder designed for creating richer representations by applying quantile encoding for a set of quantiles.

Parameters
verbose: int

integer indicating verbosity of the output. 0 for none.

quantiles: list

list of floats indicating the statistical quantiles. Each element represent a column

m: float

this is the “m” in the m-probability estimate. Higher value of m results into stronger shrinking. M is non-negative. 0 for no smoothing.

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 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 ‘error’, ‘return_nan’ and ‘value’, defaults to ‘value’, which returns the target quantile.

handle_unknown: str

options are ‘error’, ‘return_nan’ and ‘value’, defaults to ‘value’, which returns the target quantile.

References

1

Quantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems, https://link.springer.com/chapter/10.1007%2F978-3-030-85529-1_14

2

A Preprocessing Scheme for High-Cardinality Categorical Attributes in Classification and Prediction Problems, equation 7, from https://dl.acm.org/citation.cfm?id=507538

3

On estimating probabilities in tree pruning, equation 1, from https://link.springer.com/chapter/10.1007/BFb0017010

4

Additive smoothing, from https://en.wikipedia.org/wiki/Additive_smoothing#Generalized_to_the_case_of_known_incidence_rates

5

Target encoding done the right way https://maxhalford.github.io/blog/target-encoding/

Methods

fit(X, y)

Fits the encoder according to X and y by fitting the individual encoders.

fit_transform(X[, y])

Encoders that utilize the target must make sure that the training data are transformed with:

get_feature_names()

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 are not included!.

get_params([deep])

Get parameters for this estimator.

set_params(**params)

Set the parameters of this estimator.

transform

fit(X, y)[source]

Fits the encoder according to X and y by fitting the individual encoders.

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()[source]

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 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_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.